REVIEWARTICLE

Emotional Competence and its Influences on Teaching and Learning

Pamela W. Garner

Published online: 2 May 2010 # Springer Science+Business Media, LLC 2010

Abstract This article provides an interdisciplinary review of theory and research linking aspects of emotional competence to learning and school-related outcomes across childhood. Drawing upon work in developmental psychology, educational psychology, and teacher education, this review also discusses the role of teachers in socializing students’ emotions and considers the strategies and the challenges they face in regulating their own emotions in the classroom context. Future directions for research in this area are proposed.

Keywords Emotional competence . Emotion understanding . Emotion regulation .

Teacher emotions

Emotional competence is a generic term that has been applied to many types of emotion- related skills. Early research focused on understanding more about the underlying essences of the construct. Most recently, emotional competence has been conceived as including the awareness of emotion, the ability to use and understand emotion-related vocabulary, knowledge of facial expressions and the situations that elicit them, knowledge of the cultural rules for displaying emotion, and skill in managing the intensity of one’s emotional displays in ways that are appropriate to the audience and the situation (Cole et al. 2004; Eisenberg and Spinrad 2004).

Emotions are thought to be rooted in relationships because they provide information that is most meaningful in the context of social exchanges (Saarni 1999; Thompson 1991). Thus, for the past 15 years or so, researchers interested in emotions have tended to focus on individual differences in emotional competence and the implications of these differences for understanding social relationships. These studies have generally shown that, among children, the understanding of emotion is associated with peer popularity, the ability to initiate social exchanges with peers, positive conceptions of peer experiences, and prosocial and empathy-related behavior (Denham 1986; Dunn 1995; Garner and Estep 2001). In

Educ Psychol Rev (2010) 22:297–321 DOI 10.1007/s10648-010-9129-4

P. W. Garner (*) New Century College, George Mason University, 4400 University Drive, MSN 5D3, Fairfax, VA 22030, USA e-mail: [email protected]

addition, children who cope with felt emotion in ways that minimize peer conflict tend to be rated as more likable by peers and have healthier friendships than other children (Fabes and Eisenberg 1992). In contrast, low levels of emotion regulation in the early years have robustly predicted long-term behavioral problems (Rydell et al. 2003).

Despite the progress that has been made with respect to understanding the role of emotional competence in the development of children’s skill in the social arena, a full understanding of the construct and its utility for human functioning requires attention to other aspects of children’s development. In this article, it is proposed that emotions are fundamental to children’s academic and cognitive competence. Specifically, research adopting both psychological and educational perspectives will be reviewed, with the goal of integrating these diverse research areas so that a more complete and influential approach to the study of the emotion–cognition link can be established and the specific emotional competencies that are most crucial for academic success can be revealed.

This article will commence with a brief overview of the theories of emotions that have been used most often to study the linkage between emotion and cognition. Then, a summary and evaluation of the empirical research on the associations between emotional and academic competence and children and youth are presented. Next, research on teacher emotions will be discussed. The article concludes with a discussion of the methodological problems that currently exist in the literature and a presentation of possible research directions for furthering the research linking emotional competence and children’s readiness for, adjustment to, and performance in school. This review is meant to be illustrative rather than comprehensive as the variation in theoretical frameworks and the accompanying variation in operational definitions, measures, and methods used across studies precludes an all inclusive review and synthesis of the research on this topic. However, by examining the existing research, the goal is to bring coherence to the findings so that we may begin to make some general conclusions regarding the connection between emotional competence and academic performance as it develops in the early years of development and changes as children progress through the grade school and adolescent years.

Emotion-Related Psychological Theories of Learning

Various theoretical perspectives have been used to understand and explain the emotion– cognition link. Motivational theories focus on how emotion regulation ability contributes to the quality of effort and the degree of persistence individuals exert in completing academic tasks (Schutz and Davis 2000). Research adopting this framework has shown that children who display curiosity about school tasks, who are engaged in learning, and maintain positive feelings when challenged academically tend to perform better in school and on standardized tests than do other children (Lepper et al. 2005). Research has shown that experiencing positive emotion may improve problem-solving ability, facilitate recall of affectively neutral and positive information, and improve decision making (Estrada et al. 1994; Isen and Shalker 1982). Alternatively, attentional control theory posits that optimal cognitive performance is most likely to occur when attentional resources are dispersed widely than when focused narrowly on a specific task (Eysenck et al. 2007). The idea is that allocating attentional resources to the management of emotion may detract from the ability to think and listen (Blankstein et al. 1989), skills that are important for learning both within and outside of school. In his differential emotions theory, Izard suggests that the emotion of interest is a central motivator and stimulant for creative thought and action and that it accounts for selective attention (Izard (1991). More recently, this theory has been expanded to consider

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how overall emotion knowledge allows for the use of appropriate emotion in the completion of tasks that require high levels of focused attention (Izard 2009).

Emotion-Related Educational Theories of Learning

Educational researchers have also done significant theoretical work linking emotion and cognition. Some of this work has borrowed from social psychological theories that focus on goals and motivation (e.g., Linnenbrink 2006). For example, according to the control value theory of achievement emotions proposed by Pekrun (2006), individual’s appraisals of control and their values regarding achievement are central to the arousal of emotions associated with learning in school. Some of these emotions are related to the effort associated with completing the academic task (e.g., enjoyment, frustration, and boredom), and other emotions are associated with children’s experiences of success or failure in completing the task (e.g., joy, hope, pride, anxiety, hopelessness, shame, and anger).

Still another viewpoint of the emotion–cognition linkage is characterized by the premise that student goals and the emotions they experience in relation to these goals are influenced by the social and historical context. That is, students’ emotional expressions are a function of how they were socialized as well as how the emotion is currently being experienced (Schutz et al. 2006). This approach resembles the emotion socialization conceptual framework adopted by developmental scholars to understand how children learn about emotions from parents (Eisenberg et al. 1998).

Although less well established than the previously discussed theories, another important and more controversial perspective of the emotion–cognition connection is the emotional intelligence (EI) framework. EI is a term that has been applied to the ability to correctly perceive facial, behavioral, and situational cues of emotions in the pursuit of a social goal (Mayer and Salovey 1997). Whereas EI has similarities to the emotional knowledge construct discussed earlier, it encompasses personality and motivational traits and other affective skills such as empathy, self-concept, and assertiveness (Zeidner et al. 2002) that are not captured in traditional measures of emotion understanding, such that a strong case could be made that EI reflects something different than emotional knowledge.

Several theoretical perspectives have been proposed to explain the association between emotion and cognition. Most emphasize the importance of having awareness and understanding of emotional cues and the ability of regulating and managing the experience and expression of emotion, although they sometimes differ in the emphasis that they place on intrapersonal versus interpersonal cues. In the immediately following sections, I will survey the research linking these two aspects of emotional competence to children’s cognition and learning, after first providing a chronology of how each develops.

Method

Literature search strategy

Several methods were used to locate the studies included in this review. To begin, a computer search of Psychological Abstracts (PsychLIT) was conducted using search terms connected directly with the understanding and regulation of emotion. For emotion knowledge, relevant articles were chosen if they specifically mentioned the word emotion knowledge, emotion situation knowledge, emotion understanding, affective knowledge,

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emotional/affective perspective taking, emotion recognition, emotion perception, emotional/ affective intelligence, emotional display rule knowledge or if the words knowledge, understanding, intelligence, recognition , or perception were paired with a specific emotion type (e.g., happiness, sadness, anger, and fear). These terms were chosen based on the work of prominent researchers and theorists who study children’s knowledge of emotions, including Harris (2000), Izard (2001), Michalson and Lewis (1985), and Saarni and Harris (1999). Emotion regulation was the sole term used to search for the emotion regulation studies. Once located, studies were chosen for inclusion in this review if they met the criteria of focusing on emotion knowledge and/or emotion regulation in conjunction with academic or cognitive outcomes in school settings. From this search, relevant articles referenced by those articles located using the computer search method were also located. Finally, a manual search was conducted of the following journals published between 1979 until 2009: Child Development, Developmental Psychology, Social Development, Early Education and Development, Journal of School Psychology, School Psychology Quarterly, Early Childhood Research Quarterly, Educational Psychologist, Educational Psychology Review, Emotion, Cognition and Emotion, Journal of Nonverbal Behavior, and Teacher and Teacher Education.

Emotion Knowledge

Emotion knowledge is a construct that has been studied across the life span, and it has come to play a central explanatory role in predicting the quality of social relationships in many different contexts for people of all ages. However, investigations of emotion knowledge typically focus on young children and, as will become clear later, those that include older individuals tend to be limited in the modes of assessment that are used to measure the emotion knowledge construct.

Emotion knowledge in early childhood

An important precursor of emotion knowledge is the ability to talk about internal states, a skill that emerges early in the second year of life. Indeed, some have even argued that the ability to talk about emotions is one of the earliest indicators of the emotion–cognition connection (Izard 2009). Young toddlers’ ability to talk about internal states that include feelings is thought to demonstrate early self-understanding, moral awareness, and emotion regulation (Bretherton et al. 1986; Brown and Dunn 1991). Some 2-year olds can correctly sort photographs of facial expressions of happiness and sadness and can distinguish between high and low intensities of these emotions (Nelson and DeHaan 1997). Still, the bulk of research on emotion knowledge in early childhood has been concerned with children at least 3 years of age. These studies have considered children’s understanding of emotional expressions and their knowledge of the normative reactions to emotion-eliciting situations (i.e., emotion situation knowledge). Knowledge of emotional expressions is concerned with the comprehension verbal labels for facial displays of emotion, whereas emotion situation knowledge involves the ability to reason about the contextual and situational cues of emotion.

Children learn the labels and expressions and situations associated with happiness, sadness, and anger before they learn about the facial and situational cues of fear and surprise. These findings hold regardless of whether emotion knowledge is assessed with emotion recognition tasks where children are asked to identify a facial label after first being given the verbal label or whether the stimuli focus on hypothetical or live events, line

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drawings, real people or photographs of real people posing emotions, or puppets who “act out “ emotions vocally, behaviorally, and contextually (Denham and Couchoud 1990; Fabes et al. 2001; Garner and Estep 2001; Michalson and Lewis 1985), but children may perform better if they are asked to interpret emotions of children belonging to their own ethnic group (Glanville and Nowicki 2002). Importantly, these results have been demonstrated in ethnic minority, low-income, and behaviorally maladjustment children (e.g., Downs et al. 2007; Garner et al. 1994; Smith and Walden 1999).

Emotion knowledge in school-age and adolescent children

As children move into grade school, they progress beyond the ability to label and understand facial expressions and emotion-eliciting situations to a fuller understanding of emotions that includes knowledge of complex emotions, an understanding that individuals can feel two different emotions about the same event, and emotional display rule knowledge. Higher- level emotion understanding requires a consideration for an audience (e.g., peers and teachers) and an awareness of the cultural norms for emotional expression (Saarni 1999).

Although some preschoolers can understand pride when facial cues are accompanied by behavioral cues such as chest expansion and pulled back shoulders (Tracy et al. 2005), school-age children have a more advanced understanding of shame, pride, and guilt. This is significant for understanding the emotion–cognition link because these emotions are commonly expressed in schools. Unlike most preschoolers, school-age children are also aware of the possibility that the same individual can experience different emotions simultaneously. For example, a child can experience sadness at the thought of moving away and leaving behind a treasured friend while also experiencing excitement at the prospect of meeting a new best friend. School-age children are also less reliant on external cues than younger children when evaluating the emotions of others (Harris 1989).

School-age children also know the importance of hiding their internal feelings because they understand better than younger children the negative consequences associated with expressing the “wrong” emotions during social interactions (Parker and Gottman 1989). Sometime around third grade, children begin to understand that it is sometimes necessary to appear regulated even when physiologically and affectively aroused (Gross and Levenson 1997). This is referred to as emotional display rule knowledge. Display rule knowledge involves an understanding that one can conceal his/her internal feelings by maintaining a neutral facial expression, varying the intensity of the emotional expression, or masking the “true” emotion by displaying a different emotion (Matsumoto et al. 2005; Saarni 1979). This type of emotion knowledge operates to help children to identify situations in which the expression of certain emotions would be socially unacceptable (e.g., Zeman and Garber 1996), such as expressing sadness, disappointment, or embarrassment at earning a poor grade or regulating the expression of pride at receiving an excellent grade when a friend receives a low mark. These more advanced skills are predicated upon the ability to understand emotional expressions and the normative response to emotion-eliciting situations described earlier, and some children show rudimentary ability to express complex emotions and a beginning understanding of the rules for displaying emotions as early as toddlerhood (Barrett et al. 1993).

As suggested in the research described above, the understanding of the most basic emotions (i.e., happiness, sadness, anger, fear, and surprise) is well established by the beginning of the middle-school grades (McClure 2000). However, children continue to show improvements in the understanding of emotion-eliciting situations and in the understanding of subtle cues of emotion as they move into the adolescent years and beyond (Markham and Adams 1992; Thomas et al. 2007). Interestingly, individuals began

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to show age-related declines in the understanding of emotions at about 30 years of age (Mill et al. 2009), particularly for the emotions of anger and sadness (Phillips et al. 2002).

Emotion Regulation

Whereas emotion knowledge represents the acquisition and understanding of emotion, emotion regulation ability has to do with performance. Emotion regulation has often been defined as the ability to successfully manage emotional arousal and skill in controlling one’s internal state and the external expression of that state (Thompson 1991). The expression of positive and negative emotion must be regulated, but the greatest pull for the management of emotion occurs in response to negatively valenced emotions (Barrett et al. 2001). Some researchers focus on how the emotion is initially expressed (i.e., antecedent- focused emotion regulation) and therefore managed, whereas others focus on the outcomes of an emotion regulation strategy (i.e., a response-focused emotion regulation strategy) in their assessment of skill in this area (see Cole et al. 2004; Gross 1998). What is widely accepted, though, is that emotion regulation involves physiological, neurologic, motiva- tional, and behavioral processes. Researchers obviously differ in the extent to which they focus on singular or multidimensional dimensions of the construct. Moreover, emotion regulation, at least aspects of it, is explained by individual differences that are biologically rooted, present very early in life, and stable across time and context, points which are not always addressed in studies on this topic (Eisenberg and Spinrad 2004).

Emotion regulation in early childhood

From birth, young children experience physiological and interpersonal events that elicit extreme intense levels of both negative and positive emotions (Karraker et al. 1994). The ability to manage these negative emotional events is an important developmental achievement because the maintenance of positive affect is associated with later cognitive and social competence, and early emotional behavior is thought to be predictive of later affective skills (Cicchetti et al. 1991). The entry into toddlerhood marks a particularly challenging time for developing emotion regulation ability because aggression is at its highest level during this period in development (Shaw et al. 1994). Thus, early attempts at emotion regulation tend to be aided by parents and other adults. However, sometime during the second year of life, the expectation is that children will come to rely on their own internal resources to manage their emotions (Cicchetti et al. 1991; Kopp 1989). For instance, mothers of toddlers are less likely than mothers of infants to eliminate emotional stressors or attempt to soothe their children (Karraker et al. 1994). These maternal interventions continue to decrease as children move from toddlerhood to the preschool years (Spinrad et al. 2004). During the preschool years, children continue to show improvement in the development of emotion regulation ability and can implement carefully planned strategies (i.e., venting, actively resisting negative overtures from a peer, and seeking the assistance of a supportive adult) for responding to stimuli and situations that evoke negative emotion (Fabes and Eisenberg 1992).

Emotion regulation in school-age and adolescent children

During the middle school years, children become better at turning their attention away from behavioral emotion regulation strategies to those that are more complex and internally based,

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such as acceptance, cognitive distraction, and positive reappraisal of the emotion-eliciting event (Harris 1989). As individuals move into the adolescence and early adulthood, they become less focused on emotion-related goals for regulating emotions and more concerned with the acquisition of different skills built around “new” experiences (Carstensen et al. 2003).

Difficulty regulating emotional expression is an important predictor of problematic social relationships across the life span regardless of whether emotion regulation is conceptualized as a global ability, if regulation of specific emotions is measured, or whether antecedent-focused or response-focused emotion regulation strategies and behaviors are considered. For example, elementary children who have peer difficulties tend to be characterized as higher than other children in negative arousal and emotional reactivity (Kochenderfer-Ladd 2004), and children rated as being frequently angry at both home and school are more likely than other children to be victimized by peers (Hanish et al. 2004). Moreover, adolescent children who are unable to effectively regulate their emotions may be at increased risk for adolescent depression, the inability to seek out others for support, and disengagement (Garber et al. 1995; Silk et al. 2003). Socially incompetent children may have difficulty controlling the expression of positive emotion as well (Miller and Olson 2000). Importantly, some researchers have hypothesized that emotion regulation is predicated upon the ability to understand emotions (Cicchetti et al. 1995).

Linkages Between Emotion Knowledge and Academic and School-related Competence

Though the literature is laden with work linking challenges in the understanding, processing, and regulation of emotion to social and behavioral outcome measures, relatively little research has examined the question of how aspects of emotional competence relates to school outcomes across childhood. This is despite the fact that emotion-related problems are a frequent cause for referral of school children for psychological services (Greenberg et al. 1991). Indeed, emotion- related problems in young children represent one of the most challenging issues for educators because of the heightened affect that is often present in the classroom (McCabe et al. 2000), and educational researchers have hypothesized that the awareness, appraisal, and understand- ing of emotions are critical to the creation of a positive classroom climate that encourages effective instructional engagement for students and teachers (Meyer and Turner 2006).

Language competence

In early childhood, studies considering the linkage between emotion knowledge and cognitive outcomes have focused, to a large extent, on language competence as the measure of cognitive competence. For example, a study conducted by Colwell and Hart (2006) demonstrated that emotion knowledge was positively associated with preschoolers’ language competence as measured by the Peabody Picture Vocabulary Test. Children’s ability to label emotions was assessed with drawings of an adult female expressing happiness, sadness, anger, and fear. Scores on this measure were aggregated with scores on a measure that assessed the children’s understanding of the situational determinants of these same emotions using hypothetical vignettes that involved a female child. The authors do not explain why pictures of an adult female were used in the affective labeling task and pictures of a female child were used in the emotion situation knowledge task or discuss how this might have impacted children’s emotion knowledge scores.

The link between language competence and emotion knowledge has also been demonstrated in research on European children. Specifically, Cutting and Dunn (1999)

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assessed multiple forms of child language, which included measures on the standardized British Picture Vocabulary Scale, a measure of receptive language and children’s ability to retell a story from a picture book, and a measure of expressive language. From the narrative assessment, the length and grammatical complexity of the children’s sentences were also determined. Affective labeling and knowledge of situational cues of emotion were included as separate components of children’s emotion knowledge. As before, receptive vocabulary was positively related to both types of emotion knowledge, as was narrative performance on the story retelling and sentence length and complexity. Interestingly, grammatical complexity was associated only with the more advanced type of emotion knowledge, namely, emotion situation knowledge. In a study of first- and second-grade children, Trentacosta et al. (2006) found an association between verbal ability as assessed by the Stanford-Binet vocabulary subtest and children’s knowledge of facial expressions and their understanding of prototypical situations related to happiness, sadness, anger, and fear as assessed with the Assessment of Children’s Emotion Skills Scales developed by the authors.

Other research involving school-age children has also shown links between emotion knowledge and vocabulary scores. Using a small sample, Bajgar and her colleagues conducted a validation study of a “new” emotion knowledge task called the Levels of Emotional Awareness Scale for Children. Children aged 9 to 12 years were presented 12 evocative interpersonal scenarios that were developed to elicit feelings of anger, fear, happiness, or sadness and asked to provide written responses to the following two questions “How would you feel?” and “How would the other person feel?” Children’s performance on the Levels of Emotional Awareness Scale for Children was positively related to their scores on the vocabulary subtest of the Weschler Intelligence Scale for Children-Revised and to another measure of language competence (i.e., the total number of words children used to respond to the emotion scenarios; Bajgar et al. 2005).

Some have argued that the linkage between emotion knowledge and language competence is due to the fact that most tasks of emotion understanding require verbal fluency for the children to be successful (Shields et al. 2001). Others, however, have suggested that language competence provides an important vehicle for children to label, articulate, and acknowledge their own and others’ emotional feelings and experiences so that the specific act of learning to talk about emotions is adaptive and functional (Izard 2009).

Attentional competence

As suggested by attentional control theory, problems understanding emotions might also contribute to children’s inability to attend during instructional tasks. Children with higher emotion knowledge scores appear to be better than other children at focusing and sustaining attention in the classroom (Nelson et al. 1999). Emotion knowledge at the beginning of the school year also predicts attentional competence at the end of the year for early elementary school children. In a study of first- and second-grade children, (Trentacosta et al. 2006) assessed children’s emotion knowledge using the Assessment of Children’s Emotion Skills Scales, which measures knowledge of facial expressions and children’s understanding of prototypical situations related to happiness, sadness, anger, and fear. Children were also asked to nominate classmates who expressed the highest level of happiness, sadness, and anger. Attentional competence was assessed using the teacher-rated cognitive concentration subscale of the Teacher Observation of Classroom Adaptation. Fall measures of emotion knowledge predicted attentional competence assessed later that spring. Children nominated by their peers as being happier than their peers were rated by teachers as being more

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attentionally competent than other children. On the other hand, those children with higher sadness and anger nominations were rated as less attentionally competent. Given that these studies are correlational, it is also plausible that attentional control contributes to emotion knowledge. For example, preschoolers’ attentional control as assessed by teacher report longitudinally predicts children’s knowledge of the prototypical response to emotion- eliciting situations (Schultz et al. 2001).

Overall school adaptation

In other work, Izard and his colleagues assessed economically disadvantaged preschoolers’ ability to point to the correct emotional expression and their ability to produce a label for nine basic emotions. These two scores were aggregated to reflect a total emotion knowledge score. At 9 years of age, skills in reading, math, and motivation to succeed were evaluated by teachers. Preschoolers’ scores on the emotion knowledge composite meaningfully predicted social skills, behavioral dysregulation, and academic competence at age 5 and positively predicted academic competence at age 9, even after controlling for gender, language competence, and temperament (Izard et al. 2001). In similar work, Shields and her colleagues assessed the emotion knowledge of Head Start enrolled preschoolers in the Winter months using a composite of emotion situation knowledge and affective perspective taking and correlated it with teachers’ ratings of the children’s academic skills (i.e., reading readiness, number recognition and counting, language skills, and compliance to classroom rules) at year end. The emotion knowledge composite was positively related to teacher- rated school adjustment and adaptation (Shields et al. 2001). However, Miller et al. (2006) reported that emotion knowledge was unrelated to teachers’ ratings of children’s cooperation and self-control in the classroom, after controlling for age and verbal ability.

Intelligence and achievement test scores

Among preschoolers, an association between emotion knowledge scores and performance on intelligence quotient (IQ) tests has also been found among normally developing, maltreated, and children at environmental risk because of low socioeconomic status (SES) and maternal education (Garner and Waajid 2008; Pears and Fisher 2005; Sullivan et al. 2008), and toddlers’ IQ scores positively predict emotion knowledge 2 years later (Bennett et al. 2005). Oddly, emotion knowledge is not associated with young children’s perceptions of their school ability, at least in the early elementary years (Donelan-McCall and Dunn 1997), but emotion understanding may make children more sensitive to teacher criticism about their performance and behavior (Cutting and Dunn 2002). Although not specified in the work itself, these findings lend themselves to an interpretation consistent with the motivational theories proposed by Linnenbrink (2006), Pekrun (2006), and even the differential emotions theory of Izard (2009), who propose that emotion can impact the amount of interest and attention that is devoted to an academic task and that certain emotions become activated at the anticipation of certain academic-related experiences, such as test taking. This may work well when the emotions are positive and not so well when the activated emotions are negative, though some types of negative emotions (e.g., anxiety and frustration) can be transformed into interest or motivate a student to put more effort into the learning tasks, whereas other types of negative affect (e.g., anger and sadness) may lead to abandonment of the task altogether (Efklides 2006).

Considerably less is known about the specific role of emotional competence in predicting school-age children’s IQ and achievement test scores. However, consistent with

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the findings reported above, Farmer and his colleagues assessed kindergarteners’ emotion situation knowledge using the Emotion Recognition Questionnaire developed by Ribordy et al. 1988) and found that it, along with performance on the Weschler Intelligence Scale for Children-Revised, predicted third-grade academic grades (Farmer et al. 2002). In another study, Collins and Nowicki (2001) administered the Diagnostic Analysis of Nonverbal Accuracy (DANVA; Nowicki and Duke 1994) to low- and middle-income African American children averaging 10 years of age and investigated whether their scores were associated with children’s achievement scores on the Iowa Basic Skills. The DANVA assesses children’s ability to understand emotions via facial expressions, postures, gestures, and tones of voice as well as the ability to send these emotional signals to others. Interestingly, children’s ability to express affect and understand others’ affect as assessed with the DANVA was positively associated with academic achievement, but not IQ.

A significant body of research has explored the link between emotion and cognition in populations at risk for poor learning outcomes. For example, children diagnosed with attention-deficit hyperactivity disorder (ADHD) and other learning disabilities have more difficulty understanding emotions overall and anger in particular than other children (Norvilitis et al. 2000; Singh et al. 1998). In other research, the emotion knowledge of children and adults varying in levels and etiologies of mental disability was evaluated using photographs of emotional expressions normed and standardized by Ekman and Friesen (1975) and compared with that of nondisabled matched controls. Results indicated that children with mental disability were less proficient in the understanding of emotions and that those children with milder levels of disability were more accurate at recognizing emotions than those with a more moderate form of the disability (McAlpine et al. 1992). Finally, children varying in the presence of symptoms associated with the presence of learning disabilities (i.e., achievement scores below grade level, but average or above average intelligence) listened to stories designed to elicit feelings of happiness, loneliness, shame, pride, and guilt and were then asked to identify emotions from pictures devoid of facial cues. Children with more severe learning disabilities performed less well than other children on both the listening and visual emotion knowledge tasks and were less knowledgeable of emotional display rules (Bauminger et al. 2005).

Affective intervention programs

Indirect evidence for the link between emotion knowledge and school outcomes can also be gleaned from research on intervention programs. Preschool children’s participation in an emotion-based intervention program is associated with increases in emotion knowledge, emotion regulation skills, and social competence and decreased expression of negative emotions in preschool (Domitrovich et al. 2007; Izard et al. 2004; Izard et al. 2008). School-age children’s participation in emotion-based intervention programs has also been associated with increased cognitive performance, improved classroom climate, and decreased behavior problems (Cook et al. 1994; Domitrovich et al. 2007; Greenberg and Kusche 1998; Linares, et al. 2005). These programs have been successful when offered as small focused programs and when implemented in large urban public schools (Linares et al. 2005).

Recall that the global emotion knowledge construct includes the understanding of the causes and consequences of emotion, its relational context, the expected bodily sensations and facial expressions for emotions, the rules for displaying emotions in the presence of others, and the available repertoire of actions to enhance or reduce emotion (Barrett et al. 2001). Many researchers studying this topic work from a social contextual approach, investigating the role of emotion knowledge in the development of positive social

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interactions and relationships. In general, this research has shown that children who do not develop a higher-level emotion understanding are at risk for responding inappropriately in social situations (e.g., Denham 1986). Few studies have used the perspectives of the educational and psychological theories reviewed earlier in this paper to examine relations between emotion knowledge and school-related outcomes in children. For that reason, other than the social aspects linking emotion understanding, we do not fully understand the reasons for the linkages between aspects of emotion knowledge and children’s learning outcomes. Attention to these other theoretical approaches offers the opportunity to better understand how emotion knowledge or the lack of it impacts children’s learning in the classroom.

Emotional Intelligence and Linkages to Academic Outcomes

As already noted, there is some overlap between emotion knowledge and EI in that they both focus on the appraisal of emotions from facial, situational, and behavioral cues and the ability to regulate emotions through the implementation of strategies aimed at managing the experience and expression of emotion. Scores on global measures of EI have been unrelated to intelligence test scores in college-age students, but positively related to grade point average (GPA) and other indices of school success for both high school and college-age students (Marquez et al. 2006; Schutte et al. 1998). However, general mental ability as assessed with standardized measures of intelligence explains more variance in GPA than EI (Song et al. 2010), at least for Chinese students. Performance on EI measures correlates with the quality of children’s processing of emotion-related information (Austin 2005). Current views of EI also incorporate the notion that emotional skills are an important prerequisite for the creation of a democratic classroom and school environment (Elias et al. 2003). In related work, classroom management theory posits that classrooms conducive to learning are emotionally regulated (Emmer and Stough 2001) and encourage positive teacher–student interactions and collaborative peer relationships (Boekaerts 1993).

In considering these findings, it is important to point out that there has been some disagreement about the extent to which EI overlaps conceptually with emotion knowledge (Izard 2001), although higher EI is associated with the ability to identify facial expressions of emotions (Ciarrochi et al. 2001). In addition, there have been questions about the specificity of the scoring methods for EI and the adequacy of the psychometric properties of the measures (Izard 2001; Roberts et al. 2001).

Emotion Regulation: Linkages to School-related Developmental Outcomes

Most studies that focus on the role of emotions in the classroom have been concerned with the construct of emotion regulation. School entry introduces new standards for the suppression of aggressive and destructive impulses, cooperating with peers and adults outside of the family, and asserting one’s own needs without violating the rights of others (Pianta et al. 1995). Although most children experience negative affect in the school environment, these negative experiences place some children at significant risk for school refusal (Higa et al. 2002).

Emotion regulation skills have been identified as being important to the ability to focus selective attention and to apply the mental processes necessary for learning (Blair 2002). Indeed, experiencing frequent negative emotions in school is thought to narrow thoughts

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and critical thinking (Reschly et al. 2008). Further, children who are well regulated emotionally may be better able than other children to elicit behavior from others that promotes learning and more likely to be perceived by their teachers as attentive and cognitively advanced (Eisenberg et al. 2005). Negative emotionality, which includes mood swings, angry reactivity, and dysregulated emotions as evaluated by Shields and Cicchetti’s (1997) Emotion Regulation Checklist (ERC), is associated with higher levels of observed inattentive behavior and hyperactive behavior and lower levels of academic and attentional competence (Bulotsky-Shearer and Fantuzzo 2004; Keogh and Burstein 1988). In addition, Head Start children observed to be high in displays of mild negative emotion, anger, and sadness are rated by teachers to be low in teacher-rated classroom adjustment (Miller et al. 2004).

Children high in emotional intensity also tend to be less engaged than other children during a structured mother-child joint planning task (Perez and Gauvain 2005), skills that are important for learning, especially in the early years of grade school. Additionally, kindergarteners who are rated by their teachers as expressing high levels of intense negative emotions in the classroom are more likely than other children to be rated by their third grade teachers as having significant school performance problems that include having poor study skills and being unable to complete tests and follow instructions (Nelson et al. 1999). Emotion regulation problems in the early years are also predictive of early school dropout (Ensmiger and Slusarick 1992; Rose et al. 1989).

On the whole, very little attention has been paid to the exploration of possible linkages between emotion regulation and indicators of school progress and success for grade school children. This is despite the fact that school is the most common context of negative emotions for school-age children (Larson and Asmussen 1991) and that the aspects of emotion regulation that are important to school competence in the early years may be different from those needed in middle and high school. In addition, once children enter school, they must learn to manage a new set of emotions, including reactions of guilt, shame, pride, embarrassment, and boredom that are in direct relation to school performance (Thompson 1991). There is also the emotional control that is required when children are required to monitor the learning activities associated with homework (Xu 2008) and other tasks that require completion outside the confines of the classroom environment.

What is also known is that school-age children (ages 7–10 years) who report that they frequently experience positive emotions in the school environment use more adaptive cognitive and emotion-focused coping strategies (e.g., self-reliance and problems solving) and, in turn, report feeling that school work is relevant to their future aspirations and goals (Reschly et al. 2008). The ability to delay gratification, another measure of emotion self- regulation, is also associated with success in the early school years (Alexander et al. 1993). Emotion regulation is associated with academic performance and mathematics achievement and early literacy skills for kindergarteners, even after controlling for cognitive, attentional, and social factors, and IQ (Graziano et al. 2007; Ramsden and Hubbard 2002; Walcott and Landau 2004). In one study, Gumora and Arsenio (2002) evaluated children’s perceptions of their negative affect associated with academic tasks and of their overall mood and their conceptions of their academic competency. Teachers also reported on their perceptions of the children’s moods and information about the children’s grades, and achievements test scores were also collected. Findings revealed that it was the children who reported experiencing high levels of affect when engaged in academic tasks who had the lowest GPAs.

Recent research has also suggested that the processing demands associated with some forms of emotion regulation may actually reduce the regulatory resources that are available for the successful completion of cognitive tasks (Baumeister et al. 1998). However,

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although experiencing sadness seems to detract from children’s ability to recall educationally relevant material, implementing disengagement as an emotion regulation strategy rather than actively working through these feelings may work best when children are faced with time constraints associated with academic assignments (Rice et al. 2007).

In other work, a global measure of self-regulation that includes assessment of emotional, attentional, and behavioral components is a strong predictor of preschool and kindergarten children’s math and literacy skills (Blair and Razza 2007; Howse et al. 2003). The efficiency with which preschoolers deploy and distribute their attentional resources is also correlated with task behavior (Blair 2003), and effortful control, a measure of attentional regulation, is associated with children’s task persistence (Valiente et al. 2003). However, for school-age children, scores on these same measures of self-regulation are associated with literacy scores, but not math achievement (Liew et al. 2008). This inconsistency across subjects is especially important as children from different ethnic minority and economic backgrounds respond with more negative emotion to mathematics than to other subjects (Stevens et al. 2006). Preschool children’s behavioral self-regulation has also been found to predict school developmental outcomes in adolescence and adulthood (Ayduk et al. 2000; Shoda et al. 1990). Behavioral self-regulation (i.e., delay of gratification) may even mediate the association between family income and cognitive competence in school-age children (Evans and Rosenbaum 2008). However, whether this same pattern of findings emerges when a more precise measure of emotion regulation is included is not known.

As already noted, negative affect may signal to some students that more effort is needed for success and to less successful learners that the learning activity should be abandoned because it is too difficult (Efklides 2006). Thus, emotion regulation may operate as a component of resilience, such that maintaining positive emotion and experiencing moderate levels of negative emotion may encourage a student to persist in the learning task. Nevertheless, no one theory can account for the findings linking emotion regulation ability to children’s learning outcomes. Based on the available data, it appears that integrating motivational and attentional control theories with Izard’s differential emotions theory may provide the most reasonable explanation of how emotion regulation operates to predict learning. This means that the theories may work better in concert rather than in isolation to explain the detailed emotion regulatory processes that positively or negatively impact children’s learning in school.

Integrating Children’s and Teachers’ Emotions into the Classroom

Because children’s classroom emotion expression also impacts their relationship with teachers, which in turn can influence their school performance, it is important that we understand more about how child and teacher emotion intersect in the classroom. A central topic in research on emotions focuses on the socialization of emotional competence. Extensive research across a range of studies and laboratories has shown that high levels of positive parental emotion are associated with children’s knowledge of and regulation of emotion (Calkins and Hill 2007). On the other hand, parental negative emotion generally predicts low levels of child emotional competence. Talk about emotions also provides the opportunity for parents to probe their children for information about the causes and consequences of their feelings. For instance, parents use emotion-based language to clarify children's emotional states, to intensify their awareness of their own and others’ emotions, and to teach their children how to respond appropriately to emotion-related experiences (see Eisenberg et al. 1998; Thompson and Meyer 2007).

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Unfortunately, the idea of teachers as agents of emotion socialization has received limited research attention despite the fact that researchers and policy makers have long called for a greater examination of the role of teachers in the socialization of social emotional competence (e.g., Eisenberg et al. 1981). Still, emotion as a theme in research on teaching and teachers is almost nonexistent (Nias 1996), although new evidence on the topic is beginning to emerge (Schutz and Zembylas 2009). This is an important oversight because the school is an environment where the arousal of strong emotions occurs (Hargreaves 2000). Indeed, teachers cite student behavioral and emotional dysregulation among the major reasons for job stress and burnout (Byrne 1994; Friedman 1995). Although younger teachers have the greatest propensity for burnout and teachers who understand their students’ emotions may experience fewer feelings of burnout (Chang 2009), preservice teachers report that they receive very little training about how to develop social emotional skills in students (Brophy 1988) or how to mange their own internal feelings and external displays of emotion (Meyer 2009).

Like parents, teachers experience a range of emotions in response to their students’ performance and behavior, including worry, disappointment, hope, enthusiasm, and pride, among others (Hargreaves 2000), but the emotion of frustration is the number one negative emotion reported by teachers (Sutton 2007). Teachers can also be distracted and become emotionally overwhelmed by personal concerns and aspects of teaching that go beyond the classroom. Learning to attend to these emotions is critical because the inability to control one’s physiological and behavioral arousal can interfere with the quality of the teaching that occurs in the classroom. Many teachers recognize the importance of attending to student emotion as a critical component of their instructional role (Ahn 2005; McCaughtry and Rovegno 2003; Schwartz and Davis 2006; Sutton 2004), and they use their understanding of emotion to make curricular decisions and to inform their pedagogical styles and practices. A small qualitative study has also demonstrated that competent teachers attend to their own emotions as well as those of their students (Zembylas 2007).

Positive teacher emotions are associated with the use of effective teaching strategies, whereas high levels of negative teacher emotion appear to impact teachers’ motivation to teach and students’ ability to learn (Pekrun et al. 2002; Sutton and Wheatley 2003). When asked, elementary and secondary students report that their teachers often yell and vent when they are angry (Lewis 2001), but most preschool teachers are rated as overwhelmingly warm and engaging (Hyson and Lee 1996). This is important because students who have high levels of teacher warmth directed at them display higher levels of prosocial and sympathetic behavior in the preschool classroom (Kienbaum 2001), which may allow for greater opportunities for children to learn from peers and teachers. Children are also more likely to be on task and exert more effort into their academic tasks when they have a classroom teacher who displays high levels of positive affect (Davis 2003). There may be neurological reasons for this as positive emotion seems to boost the functioning of the part of the brain where the working memory is located (Perlstein et al. 2002).

Teachers also talk about emotions. Much of the work on this topic though focuses on the ways in which teachers describe the feelings they experience when teaching or talking about the relationships they have developed with particular students. In general, teachers talk of experiencing positive emotions when they perceive their students as making optimal academic progress and discuss negative emotions in relation to classroom management problems or other issues that interfere with their ability to teach well, such as poor relationships with parents and colleagues, student inattention, and/or student disengagement (Lasky 2000; Nias 1996; Sutton and Wheatley 2003). However, almost nothing is known

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about how teachers talk about their own emotions or about students’ emotions, although some research has indicated that teachers talk infrequently with students about their feelings (Hyson et al. 1990). At the same time, we do know that negative teacher emotions tend to elicit negative emotions from students (Thomas and Montgomery 1998), which could interfere with children’s motivation and ability to focus on classroom tasks. Moreover, teachers’ expression of negative emotion does not improve their mood (Totterdell and Parkinson 1999). However, teachers who express their negative feelings in a calm way and explain why they feel that way are more likely to be perceived positively by students in the later grades (McPherson et al. 2003).

Work on teachers’ classroom management skills is also relevant to research on teachers’ emotions. Teachers tend to agree on the importance of maintaining order and creating a manageable classroom environment, even when they differ in instructional goals and teaching styles (Evertson and Weade 1989). Teachers’ ability to create a positive emotional climate in the classroom is thought to be critically important to the learning that occurs in schools. In accordance with motivational theories of emotion, teachers may have an easier time creating a classroom environment conducive to learning when the majority of children in their classes are emotionally competent and affectively positive (Jennings and Greenberg 2008; Stuhlman and Pianta 2002). Still, some teachers are keenly aware of the importance of students’ emotions and their own emotions for learning, whereas others show a lack of attention to both and work instead to make the classroom affectively neutral (Bullogh 2009; Lewis 2001). There are also differences in how teachers perceive their roles in the classroom, with some teachers perceiving themselves more as the classroom leader and clear authority and others conceptualizing their role as one of a facilitator and mentor who guide children's learning (Good and Brophy 1986). There are also individual differences in the extent to which teachers express and allow emotions to be expressed in the classroom, with many teachers having difficulty regulating their classroom affect (Sutton 2007). There should also be some recognition of the fact that teacher emotions influence and are influenced by the emotional climate of the school as well as the principals, teachers, and parents with whom they interact (Hargreaves 2000; Zembylas 2007).

Interestingly, students are highly attuned to their teachers’ emotional expressions (Thomas and Montgomery 1998), and teachers’ expression of emotions can either positively influence students’ comprehension and understanding of the subject matter or detract from their learning (Hargreaves 2000; Rosiek 2003). Even in the preschool years, children show an understanding that they should control the expression of emotion in the presence of adults (Cole 1986). This undoubtedly carries over to the formal school environment. For example, as children grow older, their reactions to anger evoked by a teacher are different from those that are in response to anger caused by a peer. Students report feeling more intense negative emotion in response to teacher anger than to peer anger (Klingman and Zeidner 1993). At the same time, emotional reactions that are considered adaptive or regulated in response to a peer may be viewed as inappropriate when responding to a teacher. Klingman and Zeidner (1993) also found differences in how boys and girls respond to the negative emotions of their teachers. Specifically, adolescent boys respond to teacher anger with externalized emotions (e.g., anger) and aggressive acts that include slamming doors and creating classroom disturbances, whereas girls tend to express more internalized emotions such as sadness. Teachers tend to react less negatively to the expression of internalized emotions such as sadness, an emotion that is expressed more by girls than boys, than to anger, which is expressed more frequently by boys (Keenan and Shaw 1997). Differences in the ways that boys’ and girls’ expression of emotions may

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explain why teachers express more negativity in response to behavior of boys than girls and why they are more likely to have negative relationships with boys than with girls (Mantzicopoulos and Neuharth-Pritchett 2003; Stuhlman and Pianta 2002).

There is also evidence that the quality of teachers’ relationships with all students deteriorate as children move into the later grade school years (Feldlaufer et al.. 1988), which has been found to be correlated with lower scores on self- and teacher-ratings of social and emotional competence (Murray and Greenberg 2000). Teachers’ emotion-related responses to school-age children are especially significant because they appear to forecast student risk for school dropout as early as eighth grade (Rumberger 1995). Thus, training that is specifically aimed at helping teachers learn to deal appropriately with anger and other negative emotions expressed in the classroom may diminish the anger that they both experience and direct toward their students, which may improve the emotional climate of the classroom as well as the quality of the individual student–teacher relationships and subsequent learning that occurs in schools (Alvarez 2007). Training teachers to work with children who have difficulty understanding and regulating emotions should be a priority because they are often called on to aid children in appraising the emotions of others and to help them dampen or intensify their emotional expressions to meet the demands of the school environment.

Conclusions

In sum, two components of emotional competence have been proposed to explain school- related outcomes in children and youth, namely, emotion knowledge and emotion regulation. Despite the fact that investigations linking children’s as well as teachers’ emotional competence and behavior to children’s learning are on the rise, there is a need for continued research in this area and there are still major gaps in how the research has been applied in real-world classrooms.

Recommendations for Future Research Linking Children’s Emotional Competence to Learning

& Much of the research demonstrating these associations has focused on young children. Researchers should pay more attention to the association between emotional competence and school performance in grade school children. The quality and strength of the associations may change developmentally, and different types of emotion knowledge or emotion regulation strategies may be more or less important at grade school than at earlier developmental periods.

& Relatedly, much of the work that does include children at different points in development is cross-sectional. More longitudinal work is needed as emotional competence may be associated with certain preschool academic outcomes and not with grade school outcomes or the reasons for similar linkages across different time points might be different. It is also important to document whether the associations between emotional competence and school-related outcome variables persist into adolescence, a developmental period when knowledge of emotions and emotion regulation ability are more developed.

& As some researchers have hypothesized that emotion knowledge may increase children’s ability to regulate emotions (Wranick et al. 2007), a consideration of the interplay between emotion knowledge and emotion regulation in predicting school-

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related outcomes must also be explored, as should the potential mediating effects linking these three constructs.

& Some children come to school with deficits in the areas of emotion knowledge and emotion regulation that contribute to issues in the classroom, which in turn, could impact school performance. We need to know more about how interactions with peers and teachers provide a unique forum for learning about or expanding one’s knowledge and regulation of emotions.

& Although many affective intervention programs have been developed and evaluated, whether participation in interventions aimed at increasing attentional skills may also result in improvements in emotion regulation ability and other aspects of emotional competence contributes to children’s cognitive abilities should also be investigated (Rueda et al. 2004).

& Focusing on more discrete aspects of school performance (e.g., attention to instructions, planning, reading ability, and math ability) rather than on global constructs such as school adjustment or academic achievement or overall grades may also prove useful in understanding more about the mechanisms that link emotional competence to academic performance and the direction of effects.

& More subdomains of emotional competence should also be investigated. Different methodological techniques are needed to assess these skills. Line drawings and photographs are frequently used to measure of emotion knowledge. Emotions are dynamic and fluid and are expressed at varying levels of intensity, and the use of static stimuli may impact children’s performance on emotion knowledge measures. For instance, differences in emotional knowledge between children with high and low IQ scores all but disappear when computerized pictures depicting basic emotions at varying intensity levels are used as stimuli (Montirosso et al. 2010). Further, emotions are conveyed through multiple modes that include facial, situational, vocal, and behavioral cues, and it is important to know whether and how and if these various modes for receiving emotional information may differentially impact children’s school-related outcomes.

& The development of emotion knowledge and emotion regulation measures that are appropriate for use with children diagnosed with specific learning problems is also needed as most of the available measures, especially for emotion knowledge, were developed for use with children who are developing normally. Similarly, the types of measures of academic competence also need to be expanded to include behavioral observations, grades, direct assessments, and samples of specific school assignments across a variety of subject domains for a variety of populations.

Recommendations for Future Research Linking Teachers’ Emotions to Children’s Learning

As already noted, teachers also bring their cultural differences to school with them. Some teachers are able to adjust their attitudes about emotions and teaching styles to the culture and context of the students they teach. For example, a style of expressing intense verbal and facial displays of negative emotion may indicate concern and warmth to ethnic minority children enrolled in low-income urban schools (Gordon 1998), but may mean something else to children enrolled in affluent suburban schools. Teachers who work in urban schools tend to believe that expressions of high levels of positive and negative affect contribute to a classroom that is characterized by mutual respect and increased on-task behavior (Monroe, and Obidah 2004). Conceptions of emotional competence also vary across teachers,

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communities, and schools. Korean teachers are more accepting of young children’s emotional outbursts and dysregulation than US teachers (Hyson and Lee 1996). When researchers examine the within group variability of US teachers, they report that minority group teachers may be less sensitive to the individual differences of emotional expressions and the emotional aspects of temperament than Caucasian teachers (Franyo and Hyson 1999).

& Understanding the extent to which teachers’ judgments about and reactions to student emotions reflect their own emotional competencies, beliefs, and/or style of emotional expression rather than the behaviors and skills of the students themselves is also important if we are to effect positive change in the way they are trained to deal with the social emotional learning of their students (see Canivez and Bordenkircher 2002).

& We also need to know more about how teacher training, experience, and classroom size impact teachers’ affect in the classroom and their responsiveness and reactions to student emotion.

& A greater understanding of the strategies that teachers use to manage the daily negative emotion that they obviously experience when delivering disappointing feedback to students and balance that with the positive emotion that they experience when other students in the same classroom perform well is also needed. This balancing act has consequences both for teachers and students.

& Teachers observe children in a variety of academic and social situations that include dances, romantic encounters, and collaborative learning situations that parents cannot always witness in the home environment. Convincing teachers to implement research- based techniques for handling emotion in the schools may provide us with more objective and accurate information about the emotions of students in schools.

& Attention should also be directed at learning more about the emotion socialization behaviors of office workers, school social workers, and counselors who also spend significant time interacting with and observing students.

& Lastly, research on teacher emotions has focused on the emotion self-regulatory aspect of the emotional competence construct. Knowledge of emotions is also foundational to teaching, as teachers must correctly appraise student emotions to make sound instructional decisions and to interact with students successfully (Meyer 2009).

In short, the work in this area is in its infancy. The majority of the studies reviewed in this article are correlational, and therefore, we do not know if there is a causal relation between emotional competence and academic performance. Clearly, more refined methods are needed to examine this question more fully. The research reviewed here also suggests that a focus on developing interventions aimed at providing teachers with training about understanding and regulating their own and their students’ emotions would also prove especially useful as they may be the first to witness the effects of emotional incompetence on children’s learning. Also problematic is that much of the work reviewed above is not well linked to theory. Finally, although the goal of this paper differed from that of a statistical meta-analysis and that unpublished articles and dissertations were not solicited for inclusion, the studies reviewed are of varying quality, and thus, comparison among the studies is difficult. In some cases, muddled constructs of emotion knowledge and emotion regulation were used, which often rendered the work difficult to evaluate. It is for future research to continue to refine these constructs and the methods used to assess them. A more complete understanding of the cognitive processes involved in linking emotional competence to children’s learning in school will also be dependent on an integrated approach that takes into account both educational and developmental perspectives.

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Acknowledgment The author would like to thank Lesley Smith and Lisa Gring-Pemble for comments on an earlier version of this article.

References

Ahn, H. J. (2005). Teachers’ discussion of emotion in child care centers. Early Childhood Education Journal, 32, 237–242.

Alexander, K., Entwistle, D., & Dauber, S. (1993). First-grade classroom behavior: Its short-and long-term consequences for school performance. Child Development, 64, 801–814.

Alvarez, H. K. (2007). The impact of teacher preparation in responses to student aggression in the classroom. Teaching and Teacher Education, 23, 1113–1126.

Austin, E. J. (2005). Emotional intelligence and emotional information processing. Personality and Individual Differences, 39, 403–414.

Ayduk, O., Mendoza-Denton, R., Mishel, W., Downey, G., Peake, P., & Rodriquez, M. (2000). Regulating the interpersonal self: Strategic self-regulation for coping with rejection sensitivity. Journal of Personality and Social Psychology, 79, 776–792.

Bajgar, J., Ciarrochi, J., Lane, R., & Deane, F. P. (2005). Development of the levels of emotional awareness scale for children (LEAS-C). British Journal of Developmental Psychology, 23, 569–586.

Barrett, K. C., Zahn-Waxler, C., & Cole, P. M. (1993). Avoiders vs. amenders: Implications for the investigation of guilt and shame during toddlerhood? Emotion and Cognition, 7, 481–505.

Barrett, L., Gross, J., Christensen, T. C., & Benvenuto, M. (2001). Knowing what you’re feeling and knowing what to do about it: Mapping the relation between emotion differentiation and emotion regulation. Cognition and Emotion, 15, 713–724.

Baumeister, R., Bratslavsky, E., Muraven, M., & Tice, D. M. (1998). Ego-depletion: Is the active self a limited resource? Journal of Personality and Social Psychology, 74, 1252–1265.

Bauminger, N., Edelsztein, H. S., & Morash, J. (2005). Social information processing and emotional understanding in children with LD. Journal of Learning Disabilities, 38, 45–61.

Bennett, D. S., Bendersky, M., & Lewis, M. (2005). Antecedents of emotion knowledge: Predictors of individual differences in young children. Cognition and Emotion, 19, 375–396.

Blair, C. (2002). School readiness: Integrating cognition and emotion in a neurobiological conceptualization of child functioning at school entry. American Psychologist, 57, 111–127.

Blair, C. (2003). Behavioral inhibition and behavioral activation in young children: Relations with self- regulation and adaptation to preschool in children attending Head Start. Developmental Psychobiology, 42, 301–311.

Blair, C., & Razza, R. P. (2007). Relating effortful control, executive function, and false belief understanding to emerging math and literacy ability in kindergarten. Child Development, 78, 647–663.

Blankstein, B., Toner, B. B., & Flett, G. L. (1989). Test anxiety and the contents of consciousness: Thought listening and endorsement measures. Journal of Research in Personality, 23, 269–286.

Boekaerts, M. (1993). Anger in relation to school learning. Learning and Instruction, 3, 269–280. Bretherton, I., Fritz, J., Zahn-Waxler, C., & Ridgeway, D. (1986). Learning to talk about emotions: A

functionalist perspective. Child Development, 57, 529–548. Brophy, J. (1988). Research linking teacher behavior to student achievement: Potential implications for

instruction of Chapter 1 students. Educational Psychologist, 3, 235–286. Brown, J., & Dunn, J. (1991). ‘You can cry mum’: The social and developmental implications of talk about

internal states. British Journal of Developmental Psychology, 9, 237–256. Bullogh, R. V. (2009). Seeking eudaimonia: The emotions in learning to teach and to mentor. In P. A. Schutz

& M. Zembylas (Eds.), Introduction to advances in teacher emotion research: The impact on teachers’ lives (pp. 33–53). New York: Springer.

Bulotsky-Shearer, R., & Fantuzzo, J. (2004). Adjustment scales for preschool intervention: Extending validity and relevance across multiple perspectives. Psychology in the Schools, 41, 725–736.

Byrne, B. M. (1994). Burnout: Testing for the validity, replication, and invariance of causal structure across elementary, intermediate, and secondary teachers. American Educational Research Journal, 31, 645–673.

Calkins, S. D., & Hill, A. (2007). Caregiver influences on emerging emotion regulation: Biological and environmental transactions in early development. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 229–248). New York: Guilford.

Canivez, G. L., & Bordenkircher, S. E. (2002). Convergent and divergent validity of the adjustment scales for children and adolescents and the preschool and kindergarten behavior scales. Journal of Psychoeducational Assessment, 20, 30–45.

Educ Psychol Rev (2010) 22:297–321 315

Carstensen, L. L., Fung, H. H., & Charles, S. T. (2003). Socioemeotional selectivity theory and emotion regulation in the second year of life. Motivation and Emotion, 27, 103–123.

Chang, M. (2009). An appraisal perspective of teacher burnout: Examining the emotional work of teachers. Educational Psychology Review, 21, 193–218.

Ciarrochi, J., Chan, A. Y. C., & Bajgar, J. (2001). Measuring emotional intelligence in adolescents. Personality and Individual Differences, 31, 1105–1119.

Cicchetti, D., Ganiban, J., & Barnett, D. (1991). Contributions from the study of high risk populations to understanding the development of emotion regulation. In J. Garber & K. A. Dodge (Eds.), The development of emotion regulation and dysregulation (pp. 15–48). New York: Cambridge.

Cicchetti, D., Ackerman, B., & Izard, C. (1995). Emotions and emotion regulation in developmental psychopathology. Development and Psychopathology, 7, 1–10.

Cole, P. M. (1986). Children’s spontaneous control of facial expression. Child Development, 57, 1309–1321. Cole, P. M., Martin, S. E., & Dennis, T. A. (2004). Emotion regulation as a scientific construct:

Methodological challenges and directions for child development research. Child Development, 75, 317– 333.

Collins, M., & Nowicki, S. (2001). African American children’s ability to identify emotion in facial expressions and tones of voice of European Americans. The Journal of Genetic Psychology, 162, 334– 346.

Colwell, M. J., & Hart, S. (2006). Emotion framing: Does it relate to children’s knowledge and social behavior? Early Child Development and Care, 176, 591–603.

Cook, E. T., Greenberg, M. T., & Kusche, C. A. (1994). The relations between emotion understanding, intellectual functioning, and disruptive behavior problems in elementary- school-aged children. Journal of Abnormal Behavior, 22, 205–219.

Cutting, A., & Dunn, J. (1999). Theory of mind, emotion understanding, language, and family background: Individual differences and interrelations. Child Development, 70, 853–865.

Cutting, A., & Dunn, J. (2002). The cost of understanding other people: Social cognition predicts young children’s sensitivity to criticism. Journal of Child Psychology and Psychiatry, 43, 849–860.

Davis, H. (2003). Conceptualizing the role and influence of student-teacher relationships on children’s social and cognitive development. Educational Psychologist, 38, 207–234.

Denham, S. A. (1986). Social cognition, social behavior, and emotion in preschoolers: Contextual validation. Child Development, 57, 194–201.

Denham, S. A., & Couchoud, E. (1990). Young preschoolers’ ability to identify emotions in equivocal situations. Child Study Journal, 20(3), 153–168.

Domitrovich, C. E., Cortes, R. C., & Greenberg, M. T. (2007). Improving young children’s social and emotional competence: A randomized trail of the preschool “PATHS” curriculum. The Journal of Primary Prevention, 28, 67–91.

Donelan-McCall, N., & Dunn, J. (1997). School work, teachers, and peers: The world of first grade. International Journal of Behavioral Development, 21, 155–178.

Downs, A., Strand, P. S., & Cerna, S. (2007). Emotion understanding in English- and Spanish-speaking preschoolers. Social Development, 16, 410–439.

Dunn, J. (1995). Children as psychologists: The later correlates of individual differences in understanding of emotions and other minds. Cognition and Emotion, 9, 187–201.

Efklides, A. (2006). Metacognitive experiences: The missing link in the self-regulated learning process. Educational Psychology Review, 8, 287–291.

Eisenberg, N., & Spinrad, T. (2004). Emotion-related regulation: Sharpening the definition. Child Development, 75, 334–339.

Eisenberg, N., Cameron, E., Tryon, K., & Dodez, R. (1981). Socialization of prosocial behavior in the preschool classroom. Developmental Psychology, 17, 773–782.

Eisenberg, N., Cumberland, A., & Spinrad, T. (1998). Parental socialization of emotion. Psychological Inquiry, 9, 241–273.

Eisenberg, N., Sadovsky, A., & Spinrad, T. (2005). Associations of emotion-related regulation with language skills, emotion knowledge, and academic outcomes. New Directions for Child and Adolescent Development, 109, 109–118.

Ekman, P., & Friesen, W. V. (1975). Unmasking the face: A guide to recognizing emotions from facial clues. Englewood Cliffs: Prentice Hall.

Elias, M., Arnold, H., & Hussey, C. S. (2003). Introduction: EQ, IQ, and effective learning and citizenship. In M. Elias & H. Arnold (Eds.), EQ+IQ=best leadership practices for caring and successful schools (pp. 3–10). Thousand Oaks, CA: Corwin Press.

Emmer, E. T., & Stough, L. M. (2001). Classroom management: A critical part of educational psychology, with implications for teacher education. Educational Psychologist, 36, 103–112.

316 Educ Psychol Rev (2010) 22:297–321

Ensmiger, M., & Slusarick, A. (1992). Paths to high school graduation or dropout: A longitudinal study of a first grade cohort. Sociology of Education, 65, 95–113.

Estrada, C., Young, M., & Isen, A. (1994). Positive affect influences creative problem solving and reported source of practice satisfaction in physicians. Motivation and Emotion, 18, 285–299.

Evans, G. W., & Rosenbaum, J. (2008). Self-regulation and the income-achievement gap. Early Childhood Research Quality, 23, 504–514.

Evertson, C. M., & Weade, R. (1989). Classroom management and teaching style: Instructional stability and variability in two junior high English classrooms. The Elementary School Journal, 89, 379–393.

Eysenck, M. W., Derakshan, N., Santos, R., & Calvo, M. G. (2007). Anxiety and cognitive performance: Attentional control theory. Emotion, 7, 36–353.

Fabes, R. A., & Eisenberg, N. (1992). Young children’s coping with interpersonal anger. Child Development, 63, 116–128.

Fabes, R. A., Eisenberg, N., Hanish, L. D., & Spinrad, T. (2001). Preschoolers’ spontaneous emotion vocabulary: Relations to likeability. Early Education and Development, 12, 11–27.

Farmer, A.D., Bierman, K., and the Conduct Problems Prevention Group. (2002). Predictors and consequences of aggressive-withdrawn problem profiles in early grade school. Journal of Clinical Child and Adolescent Psychology, 31, 299–311.

Feldlaufer, H., Midgley, C., & Eccles, J. S. (1988). Student, teacher, and observer perceptions of the classroom environment before and after the transition to junior high school. Journal of Early Adolescence, 8, 133–156.

Franyo, G. A., & Hyson, M. C. (1999). Temperament training for early childhood caregivers: A study of the effectiveness of training. Child & Youth Care Forum, 28, 329–349.

Friedman, I. A. (1995). Student behaviour patterns contributing to teacher burnout. Journal of Educational Research, 88, 281–289.

Garber, J., Braafladt, N., & Weiss, B. (1995). Affect regulation in depressed and nondepressed children and young adolescents. Development and Psychopathology, 7, 93–115.

Garner, P. W., & Estep, K. M. (2001). Emotional competence, emotion socialization, and young children’s peer-related social competence. Early Education and Education, 12, 29–48.

Garner, P. W., & Waajid, B. (2008). The association of emotion knowledge and teacher-child relationships to preschool children’s school-related developmental competence. Journal of Applied Developmental Psychology, 29, 89–100.

Garner, P. W., Jones, D. C., & Miner, J. L. (1994). Social competence among low-income preschoolers: Emotion socialization practices and social cognitive correlates. Child Development, 65, 622–637.

Glanville, D. N., & Nowicki, S. (2002). Facial expression recognition and social competence among African American elementary school children: An examination of ethnic differences. Journal of Black Psychology, 28, 318–329.

Good, T. C., & Brophy, J. E. (1986). Educational psychology (3dth ed.). New York: Longman. Gordon, J. A. (1998). Caring through control: Reaching urban African American youth. Journal for a Just

and Caring Education, 4, 418–440. Graziano, P. A., Reavis, R. D., Keane, S. P., & Calkins, S. (2007). The role of emotion regulation in

children’s early academic success. Journal of School Psychology, 45, 3–19. Greenberg, M. T., & Kusche, C. A. (1998). Preventive intervention for school-age deaf children: The PATHS

curriculum. Journal of Deaf Studies, 3, 49–63. Greenberg, M. T., Kusche, C. A., & Speltz, M. (1991). Emotional regulation, self-control, and

psychopathology: The role of relationships in early childhood. In D. Cicchetti & S. L. Toth (Eds.), Internalizing and externalizing expressions of dysfunction (pp. 21–55). Hillsdale, NJ: Erlbaum.

Gross, J. J. (1998). Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74, 224–237.

Gross, J. J., & Levenson, R. W. (1997). Hiding feelings: The acute effects of suppressing negative and positive emotion. Journal of Abnormal Psychology, 106, 95–103.

Gumora, G., & Arsenio, W. F. (2002). Emotionality, emotion regulation, and school performance in middle school children. Journal of School Psychology, 40, 395–413.

Hanish, L., Eisenberg, N., Fabes, R., Spinrad, T. L., Ryan, P., & Schmidt, S. (2004). The expression and regulation of negative emotions: Risk factors for young children’s peer victimization. Development and Psychopathology, 16, 335–353.

Hargreaves, A. (2000). Mixed emotions: Teachers’ perceptions of their interactions with students. Teaching and Teacher Education, 16, 811–826.

Harris, P. (1989). Children and emotion: The development of psychological understanding. Cambridge, MA: Basil Blackwell.

Harris, P. (2000). Understanding emotion. In M. Lewis & J. Haviland Jones (Eds.), Handbook of emotions (2nd ed., pp. 281–292). New York: Guilford.

Educ Psychol Rev (2010) 22:297–321 317

Higa, C. K., Daleiden, E. L., & Chorpita, B. F. (2002). Psychometric properties and clinical utility of the school refusal assessment scale in a multiethnic sample. Journal of Psychopathology and Behavioral Assessment, 24, 247–258.

Howse, R. B., Calkins, S. D., Anastopoulos, A. D., Keane, S. P., & Shelton, T. L. (2003). Regulatory contributions to children’s kindergarten achievement. Early Education and Development, 14, 101–119.

Hyson, M., & Lee, K. (1996). Assessing early childhood teachers’ beliefs about emotions: Content, contexts, and implications for practice. Early Education and Development, 7, 59–78.

Hyson, M., Hirsh-Pasek, & Rescorla. (1990). The Classroom Practices Inventory: An observation instrument based on NAEYC’s guidelines for developmentally appropriate practices for 4-and 5- year-old children. Early Childhood Research Quarterly, 5, 475–494.

Isen, A., & Shalker, T. E. (1982). The effect of feeling state on evaluation of neutral, positive, and negative stimuli: When you accentuate the positive, “do you eliminate the negative? Social Psychology Quarterly, 45, 58–63.

Izard, C. E. (1991). The psychology of emotions. New York: Plenum. Izard, C. E. (2001). Emotional intelligence or adaptive emotions? Emotion, 1, 249–257. Izard, C. E. (2009). Emotion theory and research: Highlights, unanswered questions, and emerging issues.

Annual Review of Psychology, 60, 1–25. Izard, C. E., Fine, S., Schultz, D., Mostow, A. J., Ackerman, B. P., & Youngstrom, E. A. (2001). Emotion

knowledge as a predictor of social behavior and academic competence in children at risk. Psychological Science, 12, 18–23.

Izard, C. E., Trentacosta, C. J., King, K. A., & Mostow, A. J. (2004). An emotion-based prevention program for Head Start children. Early Education and Development, 15, 407–422.

Izard, C. E., King, K. A., Trentacosta, C. J., Morgan, J. K., Laurenceau, J., Krauthamer-Ewing, E. S., et al. (2008). Accelerating the development of emotion competence in Head Start children: Effects on adaptive and maladaptive behavior. Development and Psychopathology, 20, 369–397.

Jennings, P. A., & Greenberg, M. T. (2008). The prosocial classroom: Teacher social and emotional competence in relation to student and classroom outcomes. Review of Educational Research, 79, 491– 525.

Karraker, K. H., Lake, M. A., & Parry, T. B. (1994). Infant coping with everyday stressful events. Merrill- Palmer Quarterly, 40, 171–189.

Keenan, K., & Shaw, D. (1997). Developmental and social influences on young girls’ early problem behavior. Psychological Bulletin, 121, 95–113.

Keogh, B. K., & Burstein, N. D. (1988). Relationship of temperament to preschoolers' interactions with peers and teachers. Exceptional Children, 54, 456–461.

Kienbaum, J. (2001). The socialization of compassionate behavior by child care teachers. Early Education and Development, 12, 139–153.

Klingman, A., & Zeidner, M. (1993). School-related anger in Israeli adolescent students. School Psychology International, 14, 339–353.

Kochenderfer-Ladd, B. (2004). Peer victimization: The role of emotions in adaptive and maladaptive coping. Social Development, 13, 329–349.

Kopp, C. (1989). Regulation of distress and negative emotions: A developmental view. Developmental Psychology, 25, 343–354.

Larson, & Asmussen, L. (1991). Anger, worry, and hurt in early adolescence: An enlarging world of negative emotions. In M. E. Cotten & S. Core (Eds.), Adolescent stress: Causes and consequences (pp. 21–41). New York: Aldine de Gruyter.

Lasky, S. (2000). The cultural and emotional politics of teacher-parent interactions. Teaching and Teacher Education, 16, 843–860.

Lepper, M. R., Corpus, J. H., & Iyengar, S. S. (2005). Intrinsic and extrinsic motivational orientations in the classroom: Age differences and academic correlates. Journal of Education & Psychology, 97, 184– 196.

Lewis, R. (2001). Classroom discipline and student responsibility: The students’ view. Teaching and Teacher Education, 17, 307–319.

Liew, J., McTigue, E. M., Barrois, L., & Hughes, J. N. (2008). Adaptive and effortful control and academic self efficacy beliefs on achievement: A longitudinal study of 1st through 3rd graders. Early Childhood Research Quarterly, 23, 515–526.

Linares, L. O., Rosbruch, N., Stern, M., Edwards, M. E., Walker, G., Abikoff, H. B., et al. (2005). Developing cognitive–social–emotional competencies to enhance academic learning. Psychology in the Schools, 42, 405–417.

Linnenbrink, E. A. (2006). Emotion research in education: Theoretical and methodological perspectives on the integration of affect, motivation, and cognition. Educational Psychology Review, 18, 307–314.

318 Educ Psychol Rev (2010) 22:297–321

Mantzicopoulos, P., & Neuharth-Pritchett, S. (2003). Development and validation of a measure to assess Head Start children’s appraisals of teacher support. Journal of School Psychology, 41, 431–451.

Markham, R., & Adams, K. (1992). The effect of type of task on children’s identification of facial expression. Journal of Nonverbal Behavior, 16, 21–39.

Marquez, P. G., Martin, R. P., & Brackett, M. (2006). Relating emotional intelligence to social competence and academic achievement in high school students. Psicothema, 18, 118–123.

Matsumoto, D., Yoo, S. H., Hirayama, S., & Petrova, G. (2005). Development and validation of a measure of display rule knowledge: The display rule assessment inventory. Emotion, 5, 23–40.

Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey & D. Sluyer (Eds.), Emotional development and emotional intelligence: Educational implications (pp. 3–31). New York: Basic Books.

McAlpine, C., Singh, N. N., Kendall, K. A., & Ellis, C. R. (1992). Recognition of facial expressions of emotion by persons with mental retardation. Behavior Modification, 16, 543–558.

McCabe, L. A., Hernandez, M., Lara, S. L., & Brooks-Gunn, J. (2000). Assessing preschoolers’ self- regulation in homes and classrooms. Behavioral Disorders, 26, 53–69.

McCaughtry, N., & Rovegno, I. (2003). Development of pedagogical content knowledge: Moving from blaming students to predicting skillfulness, recognizing motor development, and understanding emotion. Journal of Teaching in Physical Education, 22, 355–368.

McClure, B. B. (2000). A meta-analytic review of sex differences in facial expression processing and their development in infants, children, and adolescents. Psychological Bulletin, 126, 424–453.

McPherson, M. B., Kearney, P., & Plax, T. G. (2003). The dark side of instruction: Teacher anger as classroom norm violations. Journal of Applied Communication Research, 31, 76–90.

Meyer, D. K. (2009). Entering the emotional practices of teaching. In P. A. Schutz & M. Zembylas (Eds.), Introduction to advances in teacher emotion research: The impact on teachers’ lives (pp. 73–91). New York: Springer.

Meyer, D. K., & Turner, J. C. (2006). Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review, 18, 377–390.

Michalson, L., & Lewis, M. (1985). What do children know about emotions and when do they know it? In M. Lewis & C. Saarni (Eds.), The socialization of emotions (pp. 117–139). New York: Plenum.

Mill, A., Alink, J., Realo, A., & Valk, R. (2009). Age-related differences in emotion recognition ability: A cross-sectional study. Emotion, 9, 619–630.

Miller, A. L., & Olson, S. L. (2000). Emotional expressiveness during peer conflicts: A predictor of social maladjustment among high-risk preschoolers. Journal of Abnormal Child Psychology, 28, 339–352.

Miller, A. L., Gouley, K. K., Seifer, R., Dickstein, S., & Shields, A. (2004). Emotions and behaviors in the Head Start classroom: Associations among observed dysregulation, social competence, and preschool adjustment. Early Education and Development, 15, 147–165.

Miller, A. L., Gouley, K. K., Seifer, R., Dickstein, S., & Shields, A. (2006). Showing and telling about emotions: Interrelations between facets of emotional competence and associations with classroom adjustment in Head Start preschoolers. Cognition and Emotion, 20, 1170–1192.

Monroe, C. R., & Obidah, J. E. (2004). The influence of cultural synchronization on a teacher’s perceptions of disruption: A case study of an African American middle-school classroom. Journal of Teacher Education, 55, 256–268.

Montirosso, R., Peverelli, M., Frigerio, E., Crespi, M., & Borgatti, R. (2010). The development of dynamic facial expression recognition at different intensities in 4- to 18-year olds. Social Development, 19, 71–92.

Murray, C., & Greenberg, M. T. (2000). Children’s relationships with teachers and bonds with school: An investigation of patterns and correlates in middle childhood. Journal of School Psychology, 38, 423–445.

Nelson, C. A., & DeHaan, M. (1997). A neurobehavioral approach to the recognition of facial expressions in infancy. In J. A. Russel & J. M. Fernandez-Dols (Eds.), The psychology of facial expression (pp. 176– 204). Cambridge, England: Cambridge University Press.

Nelson, B., Martin, R. P., Hodge, S., Havill, V., & Kamphaus, R. (1999). Modeling the prediction of elementary school adjustment from preschool temperament. Personality and Individual Differences, 26, 687–700.

Nias, J. (1996). Thinking about feeling: The emotions in teaching. Cambridge Journal of Education, 26, 293–306.

Norvilitis, J. M., Casey, R., Brooklier, K. M., & Bonello, P. J. (2000). Emotion appraisal in children with attention-deficit/hyperactivity disorder and their parents. Journal of Attention Disorders, 4, 15–26.

Nowicki, S., & Duke, M. P. (1994). Individual differences in the nonverbal communication of affect: The Diagnostic Analysis of Nonverbal Accuracy Scale. Journal of Nonverbal Behavior, 18, 9–35.

Parker, J., & Gottman, J. (1989). Social and emotional development in a relational context: Friendship interaction from early childhood to adolescence. In T. Berndt & G. Ladd (Eds.), Peer Relationships in child development (pp. 95–132). New York: Wiley.

Educ Psychol Rev (2010) 22:297–321 319

Pears, K., & Fisher, P. A. (2005). Emotion understanding and theory of mind among maltreated children in foster care. Development and Psychopathology, 17, 47–65.

Pekrun, R. (2006). The control-value theory of achievement emotions: Assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review, 18, 315–341.

Pekrun, R., Goetz, T., Titz, W., & Perry, R. (2002). Academic emotions in students’ self-regulated learning and achievement: A program of qualitative and quantitative research. Educational Psychologist, 37, 67–68.

Perez, S. M., & Gauvain, M. (2005). The role of child emotionality in child behavior and maternal instruction on planning tasks. Social Development, 14, 250–272.

Perlstein, W. M., Elbert, T., & Stenger, A. (2002). Dissociation in human prefrontal cortex of affective influences on working memory-related activity. Proceedings of the National Academy of Science, 99, 1736–1741.

Phillips, L. H., McLean, R., & Allen, R. (2002). Age and the understanding of emotions: Neuropsychological and sociocognitive perspectives. Journal of Gerontology: Psychological Sciences, 57, 526–530.

Pianta, R. C., Stienberg, M., & Rollins, K. (1995). The first two years of school: Teacher–child relationships and deflections in children’s classroom adjustment. Development and Psychopathology, 7, 295–312.

Ramsden, S. R., & Hubbard, J. A. (2002). Family expressiveness and parental emotion coaching: Their role in children’s emotion regulation and aggression. Journal of Abnormal Child Psychology, 30, 657–667.

Reschly, A. M., Huebner, E. S., Appleton, J. J., & Antaramian, S. (2008). Engagement as flourishing: The contribution of positive emotions and coping to adolescents’ engagement at school and with learning. Psychology in the Schools, 45, 419–431.

Ribordy, S. C., Camras, L. A., Stefani, R., & Spaccarelli, S. (1988). Vignettes for emotion recognition research and affective therapy with children. Journal of Clinical Child and Adolescent Psychology, 17, 322–325.

Rice, J. A., Levine, L., & Pizarro, D. A. (2007). “Just stop thinking about it”: Effects of emotional disengagement on children’s memory for educational material. Emotion, 7, 812–823.

Roberts, R. D., Zeidner, M., & Matthews, G. (2001). Does emotional intelligence meet traditional standards for an intelligence? Some new data and conclusions. Emotion, 1, 196–231.

Rose, S. L., Rose, S. A., & Feldman, J. F. (1989). Stability of behavior problems in very young children. Development and Psychopathology, 1, 5–19.

Rosiek, J. (2003). Emotional scaffolding: An exploration of the teacher knowledge at the intersection of student emotion and the subject matter. Journal of Teacher Education, 54, 399–412.

Rueda, M. R., Fan, J., McCandliss, B. D., Halparin, J. D., Gruder, D. B., Lercari, L. P., et al. (2004). Development of attentional networks in childhood. Neurologica, 42, 1029–1040.

Rumberger, R. W. (1995). Dropping out of middle school: A multilevel analysis of students and schools. American Educational Research Journal, 32, 583–625.

Rydell, A. M., Berlin, L., & Bohlin, G. (2003). Emotionality, emotion regulation, and adaptation among 5- to 8- year-old children. Emotion, 3, 30–47.

Saarni, C. (1979). Children’s understanding of display rules for expressive behavior. Developmental Psychology, 15, 424–429.

Saarni, C. (1999). The development of emotional competence. New York: Guilford. Saarni, C., & Harris, P. L. (1999). Children’s understanding of emotion. New York: Cambridge University

Press. Schultz, D., Izard, C. E., Ackerman, B., & Youngstrom, E. (2001). Emotion knowledge in disadvantaged

children: Self-regulatory antecedents and relations to social difficulties and withdrawal. Development and Psychopathology, 13, 53–67.

Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T., Golden, C. J., et al. (1998). Development and validation of a measure of emotional intelligence. Personality and Individual Differences, 25, 167–177.

Schutz, P. A., & Davis, H. A. (2000). Emotions and self-regulation during test taking. Educational Psychologist, 35, 243–256.

Schutz, P. A., & Zembylas, M. (2009). Introduction to advances in teacher emotion research: The impact on teachers’ lives. In P. A. Schutz & M. Zembylas (Eds.), Introduction to advances in teacher emotion research: The impact on teachers’ lives (pp. 3–11). New York: Springer.

Schutz, P. A., Hong, J. Y., Cross, D. I., & Osbon, J. (2006). Reflections of investigating emotion in educational settings. Educational Psychology Review, 18, 343–360.

Schwartz, E., & Davis, A. S. (2006). Reactive attachment disorder: Implications for school readiness and school functioning. Psychology in the Schools, 43, 471–479.

Shaw, D. S., Keenan, K., & Vondra, J. I. (1994). Developmental precursors of externalizing behavior: Ages 1 to 3. Developmental Psychology, 30, 355–364.

Shields, A., & Cicchetti, D. (1997). Emotion self-regulation in school-age children: The development of a new criterion Q-sort scale. Developmental Psychology, 33, 906–916.

320 Educ Psychol Rev (2010) 22:297–321

Shields, A., Dickstein, S., Seifer, R., Gusti, L., Magee, K. D., & Spritz, B. (2001). Emotional competence and early school adjustment: A study of preschoolers at risk. Early Education and Development, 12, 73–96.

Shoda, Y., Mischel, W., & Peake, P. (1990). Predicting adolescent cognitive and social competence from preschool delay of gratification: Identifying diagnostic conditions.Developmental Psychology, 26, 978–986.

Silk, J. S., Steinberg, L., & Morris, A. S. (2003). Adolescents’ emotion regulation in daily life: Links to depressive symptoms and problem behavior. Child Development, 74, 1869–1880.

Singh, S. D., Ellis, C. R., Winton, A. S., Singh, N. N., Leung, J. P., & Oswald, D. P. (1998). Recognition of facial expressions of emotion by children with attention-deficit hyperactivity disorder. Behavior Modification, 22, 128–142.

Smith, M., & Walden, T. (1999). Understanding and feelings and coping with emotional situations: A comparison of maltreated and nonmaltreated preschoolers. Social Development, 8, 93–116.

Song, L. J., Huang, G., Peng, K., Law, K., Wong, C., & Chen, Z. (2010). The differential effects of general mental ability and emotional intelligence on academic performance and social interactions. Intelligence, 38, 137–143.

Spinrad, T., Stifter, C., Donelan-McCall, N., & Turner, L. (2004). Mothers’ regulation strategies in response to toddlers’ affect: Links to later emotion self-regulation. Social Development, 13, 40–55.

Stevens, T., Olivarez, A., & Hammond, D. (2006). The role of cognition and emotion in explaining the mathematics achievement gap between Hispanic and white students. Hispanic Journal of Behavioral Sciences, 28, 161–186.

Stuhlman, M. W., & Pianta, R. C. (2002). Teachers' narratives about their relationships with children: Associations with behavior in classrooms. School Psychology Review, 31, 148–163.

Sullivan, M., Bennett, D. S., Carpenter, K., & Lewis, M. (2008). Emotion knowledge in young neglected children Child Maltreatment, 13, 301–306.

Sutton, R. E. (2004). Emotional regulation goals and strategies of teachers. Social Psychology of Education, 7, 379–398.

Sutton, R. E. (2007). Teachers’ anger, frustration, and self-regulation. In P. A. Schutz & R. Pekrun (Eds.), Emotions in education. San Diego: Elsevier.

Sutton, R. E., & Wheatley, K. F. (2003). Teachers’ emotions and teaching: A review of the literature and directions for future research. Educational Psychology Review, 15, 327–358.

Thomas, J. A., & Montgomery, P. (1998). On becoming a good teacher: Reflective practice with regard to children’s voices. Journal of Teacher Education, 49, 372–380.

Thomas, L. A., De Bellis, M. D., Graham, R., & La Bar, K. S. (2007). Development of emotional facial expression in late childhood and adolescence. Developmental Science, 10, 547–558.

Thompson, R. A. (1991). Emotional regulation and emotional development. Educational Psychology Review, 3, 269–307.

Thompson, R. A., & Meyer, S. (2007). Socialization of emotion in the family. In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 249–268). New York: Guilford.

Totterdell, P., & Parkinson, B. (1999). Use and effectiveness of self-regulation strategies for improving mood in a group of trainee teachers. Journal of Occupational Health Psychology, 4, 219–232.

Tracy, J. L., Robinson, R. W., & Lagattuta, K. H. (2005). Can children recognize pride? Emotion, 5, 251–257. Trentacosta, C. J., Izard, C. E., Mostow, A. J., & Fine, S. E. (2006). Children’s emotional competence and

attentional competence in early elementary school. School Psychology Quarterly, 21, 148–170. Valiente, C., Eisenberg, N., Smith, C. L., Reiser, M., Fabes, R. A., Losoya, S., et al. (2003). The relations of

effortful control and reactive control to children’s externalizing problems: A longitudinal assessment. Journal of Personality, 71, 1179–1205.

Walcott, C. M., & Landau, S. (2004). The relation between disinhibition and emotion regulation in boys with attention deficit hyperactivity disorder. Journal of Clinical Child and Adolescent Psychology, 33, 772– 782.

Wranick, J., Barrett, L. F., & Salovey, P. (2007). Intelligent emotion regulation: Is knowledge power? In J. J. Gross (Ed.), Handbook of emotion regulation (pp. 393–407). New York: Guilford.

Xu, J. (2008). Validation of scores in the Homework Management Scale for high school students. Educational and Psychological Measurement, 68, 304–324.

Zeidner, M., Roberts, R. D., & Matthews, G. (2002). Can emotional intelligence be schooled? A critical review. Educational Psychologist, 37, 215–231.

Zeman, J., & Garber, J. (1996). Display rules for anger, sadness, and pain: It depends on who is watching. Child Development, 67, 957–973.

Zembylas, M. (2007). Emotional ecology: The intersection of emotional knowledge and pedagogical content knowledge in teaching. Teaching and Teacher Education, 23, 355–367.

Educ Psychol Rev (2010) 22:297–321 321

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Journal of Behavioural Sciences, Vol. 20, Number, 2010

Identification of Learning Difficulties among Children Studying in Public Sector Schools

Farhana Kazmi

Psychology Department, Hazara University, NWFP, Pakistan Tahir Pervez

Pakistan Military Academy Campus Abbotabad, National University of Science and Technology, Pakistan

Present aimed to identify learning difficulties among children enrolled in Government / public sector schools in Pakistan. Two indigenously designed questionnaires were used for data collection. Data was collected from 19 schools under the administration of Municipal Corporation (Local Govt) and Punjab Education Department. The strength of the students enrolled in selected classes was 4849. Results indicate that the prevalence of disability stands, 6.06 % in all. Study offers further guidelines to the researchers to explore various unidentified dimensions of LDs with reference to socio economic, ecological and genetic factors.

Key terms: Learning Difficulties, Public Sector Schools

Literarily, learning difficulty is employed to indicate educational problems of the students when their class achievements are substantially below what is expected by their intelligence level or ability to learn (Pastor, & Reuben, 2002). Conceptually, it is an imperfect ability to; listen, speak, read, write and spell or to do simple mathematical calculations (NIMH, 2006; Pastor, & Reuben, 2002). Some of the students with learning difficulties reveal problems in storing information in memory as well as retrieving it from memory (Snowling, 2000) and it causes to lower their academic achievements. Due to the neurological origin, LDs are also named as developmental disorders (Kauchali, & Davidson, 2006). However, all such students require some help and attention to improve their learning process (NIMH, 2006) and they remain successful in their studies even up to the university level (Fullarton, 2008). It has been observed that generally, these students cannot try harder, unable to pay closer

*Correspondence concerning this article should be addressed to Dr. Farhana Kazmi, Assistant Professor, Psychology Department, Hazara University, NWFP, Pakistan. Email: [email protected]

Kazmi and Pervaiz

attention, need help to learn but at the same time they are not similar to other children who are enrolled in special education being mentally deficient/ retarded (Pastor, & Reuben, 2002).

LDs are generally divided in to six categories with reference to functional problems, such as: dyslexia (reading problem), dyscalculia (numeric problem), Dysgraphia (writing problem), dyspraxia (problem in running and jumping), auditory processing disorder and visual processing disorder (Wong, 1996; Kemp, Segal, & Cutter, 2009). Experts encompass behavioural and emotional problems in LDs (Lindsay, 1984; Watts, 1985; Rubenzer, 1988; Diakakis, Gardelis, Ventouri, Nikolaou Koltsida, Tsitoura et al., 2008). However, recent studies have shown that such problems may arise as a result of their poor performance in studies, methodological pitfalls and developmental issues (Huntington, 1993; Saddler, & Buckland, 1995) and due to stress (Tomblin, Zhang, Buckwalter, & Catts, 2000; Hill & Wigfield, 1984; Svetaz, Ireland, & Blum, 2001). Behavioural problems are further subdivided into temperamental /emotional (Mather & Goldstein, 2001) and antisocial behaviour such as destructiveness, aggression, disobedience, bullying other children and angry reaction toward school staff (Kemp, Segal & Cutter, 2009). Attention Deficit Hyperactivity Disorder (ADHD) has also been grouped with learning disabilities (Lyness, 2007).

The factor of IQ is significant in the process of identification of LDs. Studies have shown that LDs are independent of individual’s IQ (Siegel, 1989; Vaughn, Levey, Coleman, & Bos, 2002; Vellutino, Fletcher, Snowling, & Scanlon, 2004; Woolley, 2005; Lyness, 2007; Zachou, & Zachos, 2008; Glassberg, Hooper, & Mattison, 1999). This basic feature differentiates LDs from mental retardation (Woolley, 2005). Siegel (1989) has concluded that poor readers at a variety of IQ levels showed similar reading, spelling, language and memory deficits. Another study also has supported that such individuals remain successful in their studies even up to the higher level (Fullarton, 2008).

It has been reported that the symptoms of LDs significantly overlap with each other (Dykman, & Ackerman, 1991; Kaplan, Dewey, Crawford, & Wilson, 2001; Lyness, 2007; Young et al., 2002). Children with neurological problem manifest more than one impairment (Meehan, Njuguna, Mturi, Alcock, & Newton, 2006). For example LD and ADH are on a continuum, interrelated and usually coexist (Mayes, Calhoun, & Crowell, 2000; Pastor, & Reuben 2002;

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Vellutino, Fletcher, Snowling, Scanlon, 2004). Study has concluded that students with ADHD were found less proficient in writing stories than other children of their age (Newcomer, & Barenboim, 1991). Problems in spatial thinking coexist with numeration and computation to construct meaningful measurement concepts and processes (Booker, Bond, Sparrow, & Swan, 2003). Similarly, phonological awareness of the sound structure of words and the ability to manipulate sounds in words have been found key factors in the development of reading ability (Wagner, 1986; Tunmer, Chapman, Greaney, & Prochnow, 2002).

The students with LDs manifest different types of behavioural problems i.e., social inter personal and adjustment problems (Snowling, 2000; Bishop, & Leonard, 2000; Maqbool, 2002) such as loss of appetite or excessive eating, nail biting, refusing to realize responsibilities, antisocial or disruptive behaviour (Rubenzer,1988) and emotional problems and in some cases symptoms of depression as well (MartÃnez, & Semrud-Clikeman, 2004). As emotional development of these students significantly differs from normal students and these students appear to be at increased risk for severe depression and suicide, contributing factor may be methodological pitfalls and developmental issues (Huntington, 1993; Saddler & Buckland, 1995). According to a report by Snowling (2000) motivation of such student decreases which lower their self-concept, self-esteem, determination to face difficulties and they attribute their academic success or failure to their luck. Studies indicate that academic stress among the students generally gets worse as children progress through school (Hill, & Wigfield, 1984) and it may be one of the causes that the students with learning difficulty face emotional distress (Svetaz, Ireland, & Blum, 2000).

For identification of LDs, generally, indigenously designed questionnaire have been used in many studies such as; ten questions questionnaire (TQQ) (Belmont, 1984) or ten questions screen (TQS) (Mirza, Mehmood, Tareen, Davidson, & Rahman, 2008). TQQ and TQS have been used especially in under developed countries, where testing resource are limited such as; in Bangladesh (Zaman, Khan, Islam, & Durkin, 1992) in Jamaica (Thorburn et al.,1992), in Pakistan (Durkin, Hasan, & Hasan, 1995 ) and in Kenya (Mung’ala-Odera, Meehan, Njuguna Mturi, Alcock, Carter, et al. 2004).

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The "real" identification and prevalence of LD is difficult due to much dispute in definition with objective identification criteria (Lyon, 1996) and the most likely it is a result of inconsistencies in the criteria used to define LD in different studies (Semrud-Clikeman, Biederman, Sprich-Buckminster, Lehman, Faraone, & Norman, 1992). Studies reveal tremendous variations in prevalence of overall child and adolescent disorders in the general population rating from 1 to 50%, with a mean prevalence rate of 15.8%. Approximate rates of 8% for pre-schoolers and 12% among school students were recorded. Prevalence however, depends on age, gender and other factors (Roberts, Attikisson & Rosenblatt, 1998).

Different studies have reported different statistics. For instance, prevalence is quite different with reference to region and country. In USA, prevalence of LDs among common masses is 1.69% (US Census Bureau, 2004). The prevalence in Western countries has been calculated 3% (Hamilton-Kirkwood, 2001). However, in general, 5% of all public school students have been identified with LDs (Shaffer et al., 1996; Lyon, 1996). Johnson (2007) reported that mean prevalence of LDs is 2%. Louden (2000) concluded 10% to 20% and Tellioglu (2009) stated 2 to10 %. Whereas, Glassberg, Hooper and Mattison (1999) have reported 15.9 %. It has been found that 4-9 year of age is a very sensitive period for the development of the disabilities (Lindsay, 1984; Leach, Scarborough, & Rescorla, 2003; Woolley, 2005). Diakakis and colleagues (2008) identified 15.5% students as having LDs as well as behavioural problems in elementary schools with the age range of 7 to 10 years. Prevalence of dyslexic among students have been reported significantly greater from Grades 3 through grade 6 (Murphy, 2006) and a good number of students who reach in upper-primary and middle school grades have deficits in reading ability (Manset-Williamson & Nelson, 2005). A recent study in rural area of Rawalpindi (Pakistan) has reported that prevalence of learning difficulties among the children (including, visual problems, hearing problems, motor problems) is 13.5 % whereas, difficulty in reading and writing is 3.7%, visual problems is 2%, hearing problems is 2.5%, motor problems is 5.3% and fits is 2.1% (Mirza, Mehmood, Tareen, Davidson, & Rahman, 2008).

The learning difficulties persist in the course of time implies the need for a more accurate diagnosis at early stage as well as for an effective intervention (Lindsay, 1984; Zachou & Zachos, 2008). Various management strategies have been assumed for the students

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with learning disability. Triadic model of intervention i.e., doctors, therapists and the role of teachers/parents have been considered the best for identification, treatment, training and remedial work (Watts, 1985, Kemp, Segal, & Cutter, 2009). ‘Community-based rehabilitation (CBR) model’ was presented for developing countries to improve the quality of life along with intervention and treatment (Pim, & Sally, 2006).

With reference to intervention, the most important is to reduce the stress of the students (Omizo & Michael, 1982; Rubenzer, 1988 & 1987; Barker, 1987). Peer-assisted learning intervention strategies (PALS) (Fuchs, Fuchs, Mathes, & Simmons, 1997; Rohrbeck, et al, 2003; Spörer, & Brunstein, 2009) and community based rehabilitation program (Salamati, et. al. 2009) have also been found successfully employed to progress learning potential of these students. ‘Explicit self-regulatory strategy intervention’ (Nelson, & Manset-Williamson, 2005) Attention selection organization integration monitoring model (ASOIM), (Sung, Chang, Huang, & Sheng 2008) and ‘Paraphrasing strategy (RAP) (Hagaman, 2008) have been found effective for intervention of LDs.

There is a lack of comprehensive data on learning difficulties in Pakistan. A few studies have been conducted only with reference to physical, mentally handicapped children and on general mental health (Miles, & Miles, 2004; Durkin, Hasan, & Hasan, 1998; Shami, Schmitt, & Bittles, 1989; Bhatti, & Ashfaq, 2008; Hussain, 2009; Sarwat, Ali, & Ejaz, 2009) but field of learning difficulties has remained extremely ignored. Yet, some recent studies on learning difficulties provide fairly reliable statistics on LDs in Pakistan (Durkin, Hasan, & Hasan 1995; Maqbool, 2002; Rahman, Maqbool, & Zuberi, 2002). The present study was an exploratory study and it aimed at identifying LDs and their types among children studying in Government/public sector schools.

Method Sample

The study was conducted in the city area of district Jhang, agriculture based underdeveloped city, with 77 % rural population (Statistical Division, 2000). It is located in the centre of the province Punjab (Pakistan). At present Jhang is divided in to three parts i.e., Jhang Maghiāna, Seattleite Town and Jhang city (Tikkanen, 2007). Population of the present study consisted of the children studying in the government or public sectors schools. Lists of all government schools

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were obtained from district education office. A sample of 19 schools both primary (grade 1st to 5th ) and middle (grade 6st to 8th ) were selected through simple random sampling from three parts of the Jhang city, under the administration of Municipal Corporation (Local Govt) and Punjab Education Department. From these 19 schools 40 classes at primary level and 64 classes at middle level were selected. The strength of the students enrolled in these classes was 1647 and 3202 children respectively. Though the educational setup in Jhang comprised of public and private schools however, to control the variable of Socio economic status (SES), solitary Govt. schools were included in the study as being extremely low fee structure enrolled students in these schools belong to lower socio economic class. Table 1 Basic Data with Reference to the Study

Data f 1 Total schools surveyed 19

2 Total classes surveyed 114 3 Total class teachers interviewed 114 4 Total students enrolled in selected classes 4849

5 Total students identified as learning disabled 292

6 Students identified in primary classes 91

7 Students identified in primary classes 201

Assessment Measures

In the present study the holistic view of LDs was employed i.e., all those children were defined as having learning difficulty who are facing problems in; running, jumping, numeric skills, hearing and visual senses, writing, reading and speaking. The victim of seizures and those revealing any type of behavioural adjustment in class (e.g., aggression, disobedience, bullying etc) were also included. Two indigenously designed questionnaires were used for data collection. Keeping in view the operational definition and existing literature on LDs, a basic questionnaire was developed following TQQ and TQS

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(Belmont, 1984; Mirza, Mehmood, Tareen, Davidson, & Rahman, 2008).

The questionnaire consisted of 10 questions with reference to various types of LDs. Four questions were structured addressing the students’ problems of; gross motor skills, auditory, visual, and seizure; three questions were designed to identify problems related to numeracy skills, writing, reading and three questions were included to screen behavioural problems; i.e., aggression, disobedience and bullying others. Each question was further elaborated with example of each learning disability. It was assured by the teacher, through the questions, that all identified students were otherwise intellectually normal. At the end of each question, detailed nature of difficulty was also inquired from the teacher. Additional information tapped by this questionnaire was; age, grade and strength of students in the class. At the end class teachers were also asked to write some suggestive measures to improve the students’ level of achievement. Students overall progress in the class was also assessed by the teachers on three point scale (e.g. above average/average/unsatisfactory). The second questionnaire i.e. student’s conformity questionnaire, was based on all types of learning problems, the students were identified by the class teachers to further evaluate their problem to adjust in school. Validity of the questionnaires was determined through content validity. Careful identification of the LDs through basic questionnaire with the help of teachers and later interviews with the screened students on the ‘student’s conformity questionnaire’ was the sole source of validity of the instrument and the results. Procedure

The data was collected though face to face interviews. At the first stage, each class teacher from the selected classes was interviewed on basic questionnaire to identify children with learning difficulties in their classes. All these teachers were experienced and had completed teacher training courses such as B. Ed and M.Ed., before joining their services. Though all these teachers had fair understanding about the LDs by virtue of their experience and their post service training, however, in case of confusion they were explained about the signs and symptoms of LDs during the identification process to maintain the validity of the results. At the second stage the screened students were interviewed on ‘student’s conformity questionnaire for further

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validation of the findings. Because of the non availability of resources and experts in LDs in Jhang area, the degree of the students’ problems and the number of students with overlapping symptoms could not be identified

Results

Results indicate that total 91 students at primary level and 201 students at higher level were identified as having Learning Difficulties. Table 2 Prevalence of Learning Difficulties among Children in Primary Classes (N=1647)

Type of LDs f

% (n= 1647)

% (n= 91)

Dyspraxia (running & jumping)

52 1.07 18

Speech 43 0.9 15 Hearing 19 0.4 6.5 Sight 5 0.1 1.7 Complaints of Fits 14 0.3 5 Numeric Skills 62 1.3 21.2 Reading Problem 45 0.92 15 Writing Problem 33 0.68 11 Behavioural Problem 19 0.39 6.5 Total 292 6.06 100 Above table indicates prevalence and ratio of different types of

disability among the students of primary classes. Total 91 students (i.e. 5.59 %) were identified with learning difficulties.

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Table 3 Prevalence of Learning Difficulties among Higher Classes

Type of LDs f %

n=3202

%

n=201

Dyspraxia (running & jumping) 39 1.2 19.4

Speech 35 1.09 17.4 Hearing 13 0.4 6.5 Sight 4 0.12 2 Complains of Fits 8 0.3 4 Mathematical Difficulty 42 1.3 21 Reading Difficulty 30 0.94 15% Writing Difficulty 19 0.59 9%

Behavioural Problems 11 0.34 5.5 Total 201 6.3% 100%

Above table reveals prevalence and ratio of different types of

learning difficulties among children studying in higher classes. Total 201 students of 3202 (6.3 %) were identified with learning difficulties.

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Table 4 Comparison of Prevalence Rate among Children Studying in Primary Classes and Those in Higher Classes

Types of LDs

Primary classes

(1st to 5th )

Higher classes

(6th to 8th )

Total

n = 1647 n = 3202 N=4849 f f f χ2 Dyspraxia(problem in running & jumping)

13 39 52 0.92

Speech 8 35 43 3.16 Hearing 6 13 19 0.00 Sight 1 4 5 0.29 Complains of Fits 6 8 14 0.89 Mathematical Difficulty 20 42 62 0.03

Reading Difficulty 15 30 45 0.09 Writing Difficulty 14 19 33 1.95 Behavioural Problems 8 11 19 1.06 Total 91 201 292 8.42

χ2

, df 8 = 8.42 (p<0.05)

Above table reveals comparison between prevalence of different types of difficulties in primary and higher classes. The results revealed that prevalence of various type of disability is independent of age and grades of the students. However, in higher classes there were more significant hearing impairments and mathematical difficulties compared to those at primary level.

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Table 5 Academic Performance Children Identified Having LDs Academic performance f % Satisfactory (average) 259 89% dissatisfactory 33 11% Total 292 100

Table 6 Proposed Management Strategies-Class Teachers’ View

T

Discussion

Objective of the present study was to identify the prevalence and types of learning difficulties among children enrolled in Government /public sector schools. Data was collected specifically from Government schools to control the SES of the students. Validity of the findings was assured through content validity of questionnaires and the careful identification of the students. Prevalence of LD stands, 6.06 % at both level of grades. About 3% of the identified students were facing problem in; numeric skill, reading and writing. This ratio is quite associated with the study conducted in Rawalpindi region (Mirza, Mehmood, Tareen, Davidson, & Rahman, 2008). The overall figures are less than those reported by earlier researches (Roberts, Attikisson, & Rosenblatt, 1998; Louden, 2000; Glassberg, Hooper, & Mattison, 1999). Prevalence of LDs identified in the present study is higher than that reported by, Hamilton-Kirkwood, (2001), Johnson (2007) and

Strategy f % Hard work 85 29% By enhancing self-esteem 38 13% Individual attention 76 26% Sympathetic & supportive attitude towards the child

93 32%

Total 292 100

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much higher than Shaffer et al. (1996) Lyon (1996) and Diakakis, Gardelis, Ventouri, Nikolaou Koltsida, Tsitoura et al. (2008). However, the findings are concurred with Tellioglue (2009). Prevalence of hearing problem (i.e. 0.4%), visual problems (i.e. 0.1 %) motor difficulty (i.e.1.07%) and complaints of fits (i.e. 0.3%) are also very low compared to those reported by Mirza, Mehmood, Tareen, Davidson, and Rahman, (2008) in their study in Rawalpindi region. One could argue that both studies are not comparable as their study was based on common population. It was observed that because of the poor reading writing and numeric skill majority of these students were labelled by their teachers as; mentally slow, lacking will to learn, casual or sometimes as “dull” In the present study the students with mixed symptoms of LDs could not be assessed due to the limitations of resources i.e. time and testing material. This study may provide guide line to initiate further similar research with reference to various demographical variables in urban and rural areas of Pakistan. Such as variables of age, economic factor, cousin marriage or marriage in same cast/ tribes, environmental factors, birth at home and industrial areas versus agricultural areas. There is a significant need to initiate a mass level study to identify etiological factors such as; neurobiological, genetic, cognitive, linguistic, social, emotional and environmental that alone or in combination, influences the development of LDs. Conclusion

In Pakistan the problem of learning difficulties has been extremely ignored at all level and no serious efforts have been made to help out the students with LDs. The identification and then intervention of LD students is a complex issue. Though the origin of the LD cannot be eradicated, however one can boast up their morale through early detection and employing suitable models of intervention. It can improve their academic achievements and their self-esteem which will surely enable them to exploit their God gifted intellectual potentials. They need at least similar efforts and strategy as it was employed for the mentally retarded and physically disabled children in the past. Contrary to mentally handicapped LD children can be saved from frustration, emotional distress and consequently from the early termination of their education.

References

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Barker, B. (1987). Helping Students Cope with Stress. Learning, 15(5), 44-46.

Belmont, L. (1984). Final report of the international pilot study of severe childhood disability. New York: Gertrude Sergievsky Center, Columbia University.

Bhatti, A., & Ashfaq, G. (2008). Frequency of chromosomal anomalies in mentally handicapped children. Pakistan Journal of Medical Research, 47(1), 47-59.

Bishop, D. (2006). Developmental cognitive genetics: How psychology can inform genetics and vice versa. The Quarterly Journal of Experimental Psychology, 59(7), 1153–1168.

Bishop, D. V. M., & Leonard, L. B. (Eds.). (2000). Speech and language impairments in children: Causes, characteristics, intervention and outcome. New York: Psychology Press.

Booker, G., Bond, D., Sparrow, L., & Swan, P. (2003). Teaching primary mathematics (3rd ed). Sydney, Australia: Pearson Education.

Cobb, D. E., & Evans, J. R. (1981). The use of biofeedback techniques with school-aged children exhibiting behavioural and/or learning problems. Journal of Abnormal Child Psychology, 9 (2), 251-281.

Diakakis, P., Gardelis, J., Ventouri, K., Nikolaou, K., Koltsida, G., Tsitoura S., et al. (2008). Behavioural problems in children with learning difficulties according to their parents and teachers. Paediatrics, 121 (2), S100-S101.

Durkin, M. S., Hasan, Z. M., & Hasan, K. Z. (1998). Prevalence and correlates of mental retardation among children in Karachi, Pakistan. American Journal of Epidemiology, 147(3).

Durkin, M. S., Hasan, M. Z., & Hasan, Z. (1995). The ten questions screen for childhood disabilities: Its uses and limitations in Pakistan. Journal of Epidemiological Community Health, 49, 431–36.

Dykman, R. A., & Ackerman, P. T. (1992). Diagnosing dyslexia: IQ regression plus cut point. Journal of Learning Disabilities, 25(9), 574-576.

Fuchs, D., & Fuchs, L. S., Mathes, P. G., & Simmons, D. C. (1997). Peer-assisted learning strategies: Making classrooms more responsive to diversity. American Educational Research Journal, 34(1), 174-206.

79

Kazmi and Pervaiz

Fullarton, S. (2008). Successful young adults with learning disabilities. Bangalore, India: Vdm Verlag. Retrieved January 2, 2010 from http://www.flipkart.com/

Glassberg, L. A., Hooper, S.R., & Mattison, R. E. (1999). Prevalence of learning disabilities at enrolment in special education students with behavioural disorders. Behavioural Disorders, 25(1), 9-21.

Hagaman, J. L. (2008). The effects of the paraphrasing strategy on the reading comprehension of middle school students at risk for failure in Reading. Remedial and Special Education, 29(4), 222-234.

Hamilton-Kirkwood, L. (2 001). Learning disabilities. Health evidence bulletins Wales. Retrieved January 2, 2010 from http://hebw.uwcm.ac.uk.

Hill, K. T., & Wigfield, A. (1984). Test anxiety: A major educational problem and what can be done about it. The Elementary School Journal, 85(1), 105-126.

Hughes, H., Jackson, K., Du Bois, K. E. & Erwin, R. (1979). Treatment of handwriting problems utilizing EMG biofeedback training. Perceptual and Motor Skills, 48(2), 603-666.

Huntington, D. D., & Bender, W. N. (1993). Adolescents with learning disabilities at risk?. Emotional well-being, depression, suicide. Journal of Learning Disabilities, 26, (3), 159-166.

Hussain, S.A. (2009). A review of global issues and prevalence of child mental health problems. Journal of Pakistan Psychiatric Association, 6(1), 5-14.

Johnson, C. J. (2007). Prevalence of speech and language disorders in children. The encyclopaedia of early language and literacy development. 18:31:47

Kaplan, B. J., Dewey, D. M., Crawford, S. G., & Wilson, B. N. (2001). The term comorbidity is of questionable value in reference to developmental disorders: data and theory. Journal of Learning Disability, 34(6), 555-565.

Kauchali, S., & Davidson, L. L. (2006). The epidemiology of neuro developmental disorders in Sub-Saharan Africa moving forward to understand the health and psychosocial needs of children, families, and communities. International Journal of Epidemiology, 35, 689–690.

Kemp, G., Segal, J., & Cutter, D. (2009). Learning disabilities in children: learning disability symptoms, types & testing. Retrieved January 4, 2010 from http://helpguide.org/ mental/ parenting/

80

Learning Difficulties among Children

Leach, J. M., Scarborough, H. S., & Rescorla, L. (2003). Late-emerging reading disabilities. Journal of Educational Psychology, 95, 211- 224.

Lindsay, G. (1984). Screening for children with Special Needs. London: Academic Press.

Louden, W. et al. (2000). Mapping the territory, primary students with learning difficulties: Literacy and numeracy, 1 (2 & 3), 175-185.

Lyness, D'Arcy. (2007). Learning disability. Retrieved January 5, 2010 from http://kidshealth.org/

Lyon, G. R. (1996). Learning Disabilities. The Future of Children, 6 (1), 54-76.

Manset-Williamson, G., & Nelson, J. M. (2005). Balanced strategic reading instruction for upper-elementary and middle school students with reading disabilities: A comparative study of two approaches. Learning Disability Quarterly, 28(1), 58-74.

Maqbool, E. (2002). Relationship between self-concept and achievement among reading and non-reading disabled school children. PhD thesis, University of Karachi, Pakistan.

MartÃnez, R. S., & Semrud-Clikeman, M. (2004). Emotional adjustment and school functioning of young adolescents with multiple versus single learning disabilities. Journal of Learning Disability, 37(5), 411-20.

Mather, N., & Goldstein, S. (2001). Behavior modification in the classroom. Retrieved January 4, 2010 from http://www.ldonline.org/article/6030

Mayes, S. D., Calhoun, S. L., & Crowell, E. W. (2000). Learning disabilities and ADHD: Overlapping spectrum disorders. Journal of Learning Disabilities, 33(5), 417-424.

Meehan, R., Njuguna, P., Mturi N., Alcock, K.J., & Newton, C.R. (2006). Prevalence and risk factors of neurological disability and impairment in children living in rural Kenya. Journal of Epidemiology, 35, 683–688.

Miles, M., (2004). Disability & social responses in Afghanistan & Pakistan. Retrieved January 24, 2010 from http://cirrie.buffalo.edu/ /afghanpakistan/introduction.

Mirza, I., Mehmood, T., Tareen, A., Davidson, L., & Rahman, A. (2008). Feasibility study on the use of the ten questions screen by lady health workers to detect developmental disabilities in Pakistan. Journal of Pakistan Psychiatric Society, 5(2), 97.

81

Kazmi and Pervaiz

Mung’ala-Odera, V., Meehan, R., Njuguna, P., Mturi, N. Alcock, K., Carter, J. A., et al. (2004). Validity and reliability of the ten questions questionnaire for detecting moderate to severe neurological impairment in children aged 6–9 years in rural Kenya. Neuroepide miology, 23, (1-2), 67–72.

Murphy, C. (2006). Written language of children with a diagnosis of reading disabilities from Grades 3 through 6. Journal of Undergraduate Research, 8(2).

NIMH (2006). Learning disabilities: decade of the brain. NIMH, 95- 3611.

Nelson, J. M., & Manset-Williamson, G. (2005). The impact of explicit, self-regulatory reading comprehension strategy instruction on the reading-specific self-efficacy, attributions and affect of students with reading disabilities. Learning Disability Quarterly, 29(3), 213-230.

Newcomer, P. L., & Barenbaum, E. M. (1991). The written composing ability of children with learning disabilities: A review of the literature from 1980-1990. Journal of Learning Disabilities, 24(10), 578-593.

Omizo, n. M., & W. B. Michael (1982). Biofeedback-induced relaxation training and impulsivity, attention to task, and locus of control among hyperactive boys. In Rubenzer, R. L. (1988). Stress management for the learning disabled. ERIC Digest.

Pastor, P., & Reuben, C. (2002). Attention deficit disorder and learning disability: National Centre for Health Statistics. Vital Health Stat, 10(206). Retrieved January 4, 2010 from http: // www. cdc.gov/nchs/data/series/sr_10/sr10_206. pdf.

Pim, K., & Sally, H. (2006). A process for the systematic review of community-based rehabilitation evaluation reports: formulating evidence for policy and practice. International Journal of Rehabilitation Research, 29(1), 27-30.

Rahman, A., Maqbool, E., & Zuberi, S. H. (2002). Lead–associated deficits in stature, mental ability and behavior in children in Karachi. Annals of Tropical Pediatrics, 22, 301-311.

Roberts, E. R., Attikisson, C. C., & Rosenblatt, A. (1998). A. Prevalence of psychopathology among children and adolescent. In Hussain, S.A. (2009). A review of global issues and prevalence of child mental health problems. Journal of Pakistan Psychiatric Association, 6(1), 5-14.

82

Learning Difficulties among Children

Rohrbeck, C. A., et al. (2003). Peer-assisted learning interventions with elementary school students, a meta-analytic review. Contemporary Educational Psychology, 95(2), 240-257.

Rubenzer, R. L. (1988). Stress management for the learning disabled: ERIC Digest.

Saddler, C. D., & Buckland, R. L., Jr. (1995). The multidimensional perfectionism scale: correlations with depression in college students with learning disabilities. Psychological Report, 77(2), 483-90.

Salamati, P., Abolhassani, F., Shariati, B., Kamali, M., & Alehossein, S. M. (2009). Home based training: main strategy of community based rehabilitation in Iran. Pakistan Journal of Medical Sciences, 25(3), 462-467.

Sarwat, A., Ali, S. M. I., & Ejaz, M. S. (2009). Mental health morbidity in children: A hospital based study in child psychiatry clinic. Pakistan Journal of Medical Sciences, 25 (6), 982-985.

Semrud-Clikeman, M., Biederman, J., Sprich-Buckminster, S., Lehman, B. K., Faraone, S. V., & Norman, D. (1992). Comorbidity between ADDH and learning disability: A review and report in a clinically referred. Journal of American Academy of Child & Adolescent Psychiatry, 31(3), 439-48.

Shaffer, D., et al (1996). Methods for the epidemiology of child and adolescent mental disorders study. Journal of the American Academy of Child and Adolescent Psychiatry, 35, 865-877.

Shami, S. A., Schmitt, L. H., & Bittles, A. H. (1989). Consanguinity related prenatal and postnatal mortality of the populations of seven Pakistani Punjab cities. Journal of Medical Genetics, 26(4), 267-71.

Siegel, L. S. (1989). IQ Is Irrelevant to the definition of learning difficulties. Journal of Learning Disabilities, 22(8), 469-478.

Snowling, M. J. (2000). Dyslexia (2nd ed). Oxford, UK: Blackwell. Spörer, N., & Brunstein, J. C. (2009). Fostering the reading

comprehension of secondary school students through peer-assisted learning: Effects on strategy knowledge, strategy use, and task performance. Contemporary Educational Psychology, 34(4), 289- 297.

Statistics Division (2000). Census Report of District Jhang. Population Census Organization. Government of Pakistan, Islamabad.

Sung, Y., Chang, K., & Huang, J. (2008). Improving children’s reading comprehension and use of strategies through computer-based strategy training. Computers in Human Behavior, 24(4), 1552-1571.

83

Kazmi and Pervaiz

Svetaz, M. V., & Ireland, M., & Blum, R. (2000). Adolescents with learning disabilities: risk and protective factors associated with emotional well-being: findings from the National Longitudinal Study of Adolescent Health. Journal of Adolescent Health, 27(5), 340-8.

Tellioglu, T. (2006). Learning Disorders. Retrieved February 15, 2010 from http://yourtotalhealth.ivillage.com/learning- disorders.htmlpage5

Thorburn M, et al., (1992). Identification of childhood disability in Jamaica: the ‘ten questions’ screen. International Journal of Rehabilitation Research, 15, 115–27.

Tikkanen, A. (2007) (Ed). Jhang Maghiāna. In Encyclopaedia Britannica. Retrieved November 24, 2009, from Encyclopaedia Britannica Online:http://www. britannica. com/Jhang-Maghiana

Tomblin, J. B., Zhang, X. Y., Buckwalter, P., & Catts, H. (2000). The association of reading disability, behavioral disorders, and language impairment among second-grade children. Journal of Child Psychology and Psychiatry and Allied Disciplines, 41, 473-482.

Tunmer, W. E., Chapman, J. W., Greaney, K. T., & Prochnow, J. E. (2002). The contribution of educational psychology to intervention research andpractice. International Journal of Disability, Development and Education, 49(1), 11-29.

US Census Bureau, (2004) Population Estimates Statistics by country for learning disabilities. Retrieved June 4, 2010 from http://www.cureresearch.com/l/learning_disabilities /stats- country.htm

Vaughn, S., Levey, S., Coleman, M., & Bos, C. (2002). Reading instructions for students with LD and EBD: A synthesis of observation studies. The Journal of Special Education, 36, 2-13.

Vellutino, F. R., Fletcher, J. M., Snowling, M. J., & Scanlon, D. M. (2004). Specific reading disability (dyslexia): What have we learned in the past four decades? Journal of Child Psychiatry, 45(1), 2-40.

Wagner, R. K. (1986) Phonological processing abilities and reading: Implications for disabled readers. Journal of Learning Disabilities, 19 (10), 623-630.

Watts, L. (1974). Methods for Learning Disorders. New York: John Willey and Sons, Inc

Watts, N. F. (1985). New Development in Clinical Psychology. New York: John Willey & Sons.

84

Learning Difficulties among Children

Wong, B., & Fraser, S. (1996). The ABCS of learning disabilities. London: Academic Press.

Woolley, Gary. (2005). Research on reading comprehension difficulties after year four. Retrieved January 5, 2010 from http://www.canberra.edu.au/data/assets/woolley.

Zachou, A., & Zachos, I. (2008). Phonological spelling errors in the writing of Greek dyslexic children: In support of the phonological deficit theory. Retrieved January 6, 2010 from http://www.biomedcentral.com/content/

Zaman, S. S., Khan, N., Islam, S., & Durkin, M. (1992). Childhood disabilities in Bangladesh: Report on rapid epidemiological assessment of childhood disabilities in Bangladesh. Bangladesh: Bangladesh Protibondhi Foundation.

Zhang, X., & Tomblin, J. B. (2000). The association of intervention receipt with speech- language profiles and social-demographic variables. American Journal of Speech-Language Pathology, 9, 345- 357.

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REVIEW

Educating the Developing Mind: Towards an Overarching Paradigm

Andreas Demetriou & George Spanoudis & Antigoni Mouyi

Published online: 13 September 2011 # The Author(s) 2011. This article is published with open access at Springerlink.com

Abstract This essay first summarizes an overarching theory of cognitive organization and development. This theory claims that the human mind involves (1) several specialized structural systems dealing with different domains of relations in the environment, (2) a central representational capacity system, (3) general inferential processes, and (4) consciousness. These systems interact dynamically during development so that changes in each are related to changes in others. The changes in all systems and the change mechanisms are described. This theory integrates research and theorizing from cognitive, developmental, and differential psychology. Based on this theory, a model for education is proposed that specifies, first, educational priorities for different phases of development according to the cognitive developmental milestones associated with each phase. The theory also specifies how education can educate students to (1) construct mental models for the sake of conceptual change, (2) use their central representational capacity efficiently, (3) advance analogical and deductive reasoning, (4) learn how to learn, and (5) become critical and creative thinkers. The theory is offered as an overarching paradigm for the architecture, the development, and the education of the human mind.

Keywords Assessment . Cognitive development . Conceptual change . Consciousness .

Critical thinking . Education . Intelligence . Learning to learn . Mental models .

Metarepresentation . Reasoning . Working memory

Educ Psychol Rev (2011) 23:601–663 DOI 10.1007/s10648-011-9178-3

A. Demetriou (*) : G. Spanoudis Department of Psychology, University of Cyprus, P. O. Box 537, 1678 Nicosia, Cyprus e-mail: [email protected]

G. Spanoudis e-mail: [email protected]

A. Mouyi Center for Educational Research and Evaluation, Nicosia, Cyprus e-mail: [email protected]

In the second half of the twentieth century, developmental and cognitive sciences were solid enough to start systematically informing educational policy and practice (e.g., Anderson et al. 2001; Linn and Eylon 2011; Olson 2003; Stearns 2006). However, we still have a long way to go in the direction of integrating educational science with psychological science in the way modern engineering is integrated with the natural sciences. For this to be possible, a comprehensive theory of the developing mind is needed. This is the aim of the theory proposed here. This theory integrates findings and concepts from the psychology of intelligence, the psychology of cognitive development, and cognitive psychology. If properly integrated, these traditions can lead to a unified theory of learning, understanding, and development.

The integration attempted here initiates from our earlier empirical (Demetriou 2002; Demetriou et al. 1993, 2008; Demetriou and Kazi 2001, 2006) and theoretical work on cognitive development (Demetriou 1998, 2004, Demetriou et al. 2010a, b; Kargopoulos and Demetriou 1998; Demetriou and Raftopoulos 1999) and is expanded to include current research and theorizing in all three fields mentioned above. The integration of the theory with research and theory on educational implications and applications is new and is first presented here. This theory specifies the following:

1. The architecture of the mind. That is, the general cognitive structures and processes underlying understanding, problem solving, and learning across different domains and also the processes underlying the operation of specialized domains of thought. Thus, the theory goes beyond theories which emphasize the importance of different domains but underestimate the role of central processes and constrains, such as Gardner’s theory (1983), and theories which stress the importance of central processes but minimize the role of domain-specific processes, such as Jensen’s (1998) or Piaget’s (1970) theory.

2. The development of the mind. That is, the condition of the various general and domain- specific structures and processes during development, their dynamic relations, and the mechanisms which are responsible for change and learning in different phases of development. The theory integrates research and theory on developmental changes in representational and information processing capacity (e.g., Case 1985; Halford 1993; Kail 1991; Pascal-Leone 1988) with research and theory on the development of thought and consciousness (Demetriou 2000; Demetriou et al. 1993, 2002; Demetriou and Kazi 2001, 2006) to provide an integrated model explicating the development of self-aware individuals that have a strong sense of unity, a personal style of functioning, and a differentiated profile of competence and modus operanti.

3. Guidelines as to how educators can use this understanding of mind and its development to organize instructional content across diverse subject matter and to create teaching methods that are appropriate for different individuals at different phases of learning. Obviously, social understanding and interaction are extremely important for education both as a frame where cognitive processes operate and as a major goal which aspires to educate citizens well adapted in their cultural and social environment. Moreover, emotion and motivation are also important dimensions of cognitive functioning and education. Although this essay focuses on cognitive processes, for the sake of completeness, we also briefly integrate into the theory considerations about motivational and emotional processes.

This essay is addressed to three groups of scholars: cognitive and developmental scientists who would be interested to see how the cognitive, the differential, and the developmental traditions are integrated into a unified theory of the developing mind and

602 Educ Psychol Rev (2011) 23:601–663

explore the implications of this theory for education; education policy makers and planners who need to tune important aspects of educational policy, such as setting major priorities for the successive phases of education and curriculum development, with current thought and knowledge in the cognitive and the learning sciences; and, finally, this essay is addressed to classroom teachers who would like to inform their teaching practices and decisions in relation to the actual possibilities and weaknesses of their students.

Thus, this essay includes two parts. The first part presents the psychological theory. Specifically, it outlines the architecture of the human mind, its development, and the dynamic relations between the various structures and processes in real and developmental time. The second part presents the implications of the theory for education.

The Architecture and Development of the Mind

Research and theory in psychometric (Carroll 1993; Demetriou 2002; Gustafsson and Undheim 1996; Hunt 2011; Jensen 1998), cognitive (Hunt 2002), and developmental psychology (Case 1992; Demetriou 1998; Demetriou et al. 1993, 2002, 2008, 2010a, b) converge on the assumption that the human mind is a universe of processes that are organized into systems which carry out different tasks during real-time problem solving. The systems are: (1) specialized structural systems (SSS). These constitute a set of mental processes that interface with several environmental domains. (2) The representational capacity system. This system is responsible for meeting representational and information processing needs activated by the environment-oriented SSS or the systems to be discussed below. (3) The inference system is responsible for connecting and integrating information and operations vis-à-vis the goal of the moment. (4) The consciousness system is responsible for monitoring, controlling, and regulating the processes activated at a given moment. Figure 1 highlights this architecture. Below, we first outline the architecture and development of each of the systems. Then we discuss the interrelations between the various systems in real and developmental time and present relevant evidence.

SSS

Essentials and

categories

Space and time

Numbers and quantities

Interactions and causes

Agents and persons

Language

Core operators

Mental operations; rules

and principles;

Concepts, knowledge

systems, and beliefs

REPRESENTATIO NAL CAPACITY

WM, STSS

INFERENCE Induction Deduction Abduction

CONSCIOUSNESS

Self-monitoring

Self-representation

Self-regulation

Reflection and

recursion

Executive Control

Episodic Integration

Conceptual Change

Mental Models Problem Solving Self-awareness

Meta- Representation

Fig. 1 General architecture of the human mind

Educ Psychol Rev (2011) 23:601–663 603

The architecture of the specialized structural systems

Each SSS includes processes that specialize in processing information coming from different domains of the environment. Five SSS were specified by research: (1) Categorical thought deals with similarity–difference relations. Forming hierarchies of interrelated concepts about class relationships is an example of the domain of this system. (2) Quantitative thought deals with quantitative variations and relations in the environment. Mathematical concepts and operations are examples of the domain of this system. (3) Causal thought deals with cause−effect relations. Representations about causal relations between objects and persons and operations related to causality, such as trial-and-error or isolation of variables, that enable one to decipher causal relations belong to this system. (4) Spatial thought deals with orientation in space and the imaginal−iconic representation of the environment. Mental maps of places or mental images of familiar persons and objects and operations on them, such as mental rotation, belong to this system. (5) Social thought deals with social relationships and interactions. The mechanisms for monitoring non-verbal communication or skills for dealing with social interactions belong to this system. These SSS were identified by a number of studies involving participants from preschool age to adulthood. Specifically, in factorial studies, all five SSS emerge as separate factors (Case et al. 2001; Demetriou and Bakracevic 2009; Demetriou and Efklides 1989; Demetriou et al. 1993, 2002; Demetriou and Kazi 2001, 2006; Shayer et al. 1988). Moreover, together with common inferential processes, each of the SSS involves SSS-specific logical process for problem solving (Kargopoulos and Demetriou 1998).

SSS involve (1) core processes, (2) mental operations, and (3) knowledge and beliefs. Table 1 summarizes the main features of the SSS. Core processes are pre-adapted and foundational inferential traps that impose their ready-made meaning on the aspect of the environment concerned. For example, categorical perception on the basis of shape, perception of the numerocity of small sets, depth perception in space, perception of causality, and human face preference in the social domain are examples of core process in each SSS. Core processes are fundamental because they ground each domain into its respective environmental realm and they form the background for the development of the other levels of the SSS, namely, mental operations and knowledge and beliefs (Carey 2009; Cosmides and Tooby 1994; Gelman and Brenneman 1994). Mental operations arise as a result of the dynamic interactions between domain-specific core processes and the informational structures of the environment. The systems of operations within each domain emerge by differentiation of the core processes when these do not satisfy the understanding and problem-solving needs of the moment. Examples of mental operations are sorting in the categorical SSS, numerical operations in the quantitative SSS, hypothesis testing in the causal SSS, mental rotation in the spatial SSS, or emotion regulation in the social SSS. Knowledge and beliefs accumulate over the years as a result of the interactions of each SSS with the respective domain, such as time reading, money values, and rules underlying everyday transactions in the quantitative system, mental images and mental maps in the spatial system, social attributions in the social domain, etc. (Carey 1985, 2009; Demetriou et al. 1992, 1993, 2002).

The development of the SSS

Table 2 summarizes the development of the SSS. With development, each of the domains moves along three dimensions: complexity, abstraction, and flexibility. As children grow older, they become able to deal with increasingly more representations simultaneously. This

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permits them to consider alternative representations of a situation or alternative actions upon these representations or the situation itself. Increasing complexity necessitates the elaboration of relations between representations, which results into abstractions which are subsequently encoded and processed as representations, thereby leading to increasingly abstract systems of representations. Envisaging representations from the point of each other and focusing on their relations gradually frees thought from the specifics of particular representations, rendering it increasingly fluid and flexible (Table 3).

Thus, development of the SSS is a continuous process of emergence, differentiation, and integration of new representations. However, this is one aspect of development. Some changes at some periods alter how the world is viewed by the developing person. These changes may be taken as milestones that are important both from the point of view of the person’s own subjective experience and for his or her relations with the world.

Table 1 Three levels of organization of each specialized system of thought and reasoning

Domain Core processes Mental operations Knowledge and beliefs

Categorical Perception according to perceptual similarity; inductive inferences based on similarity– difference relations

Specification of the semantic and logical relations between properties, classification; transformation of properties into mental objects; construction of conceptual systems

Conceptions and misconceptions about the world

Quantitative Subitization; counting, pointing, bringing in, removing, sharing

Monitoring, reconstruction, execution and control of quantitative transformations, the four arithmetic operations

Factual knowledge about the quantitative aspects of the world, algebraic and statistical inference rules

Spatial Perception of size, depth, and orientation; formation of mental images

Mental rotation, image integration, image reconstruction, location and direction tracking and reckoning

Stored mental images, mental maps, and scripts about objects, locations, scenes, or layouts maintained in the mind

Causal Perception of overt and covert causal relations

Trial-and-error; combinatorial operations; hypothesis formation; systematic experimentation (isolation of variables); model construction

Knowledge, attributions and understanding of the reasons underlying physical and social events and the dynamic aspects of the world

Social Recognition of conspecifics, recognition of emotionally laden facial expressions

Deciphering the mental and emotional states and intentions of others; organization of actions accordingly; imitation; decentering and taking the other’s perspective

System of social attributions about other persons, their culture and their society

Reasoning Use of the grammatical and syntactical structures of language

Identifying truth in information; abstraction of information in goal-relevant ways; differentiation of the contextual from the formal elements; elimination of biases from inferential process; securing validity of inference

Knowledge about grammar, syntax and logical reasoning; metalogical knowledge about nature and justifiability of logical inferences; metacognitive awareness, knowledge, and control of inferential processes

Educ Psychol Rev (2011) 23:601–663 605

T a b le

2 M o d al

ch ar ac te ri st ic s o f th e sp ec ia li ze d d o m ai n s w it h d ev el o p m en t

A g e

C la ss

N u m b er

C au se

S p ac e

In fe re n ce

C o n sc io u sn es s

0 – 2

S im

il ar it y -b as ed

p er ce p tu al

ca te g o ri es

S u b it iz at io n -b as ed

an d

ap p ro x im

at e n u m b er s

E p is o d ic

ev en t

se q u en ce s

H o li st ic

sc en e

im ag es

S im

il ar it y -b as ed

in d u ct iv e in fe re n ce

an d ev en t b as ed

p ro b ab il is ti c in fe re n ce

B o d y – ac ti o n

co n sc io u sn es s

3 – 4

P ro to -c at eg o ri es

P ro to -q u an ti ta ti v e

sc h em

es P ro to -c au sa l

sc h em

es G lo b al

im ag es

P ri m ar y re as o n in g

D if fe re n ti at io n b et w ee n

m o d al it ie s (p er ce p ti o n

v s. k n o w in g )

5 – 6

S in g le

cr it er io n

cl as se s

C o o rd in at io n o f

p ro to -q u an ti ta ti v e

sc h em

es

C o o rd in at io n o f

p ro to -c au sa l

sc h em

es

S in g le

sp at ia l

d im

en si o n s o r

o p er at io n s

P er m is si o n ru le s

U n d er st an d in g th e st re am

o f co n sc io u sn es s an d

in n er

sp ee ch

7 – 8

L o g ic al

m u lt ip li ca ti o n

N u m b er

co n ce p ts

an d q u an ti ta ti v e

d im

en si o n s

E x p er ie n ce -b as ed

p ro to -t h eo ri es

“F lu en t”

m en ta l

im ag er y

E x p li ci t in fe re n ce

G ra sp

o f th e co n st ru ct iv e

n at u re

o f th o u g h t

9 – 1 0

L o g ic al

m u lt ip li ca ti o n

o n u n fa m il ia r co n te x t

C o n st ru ct io n o f

si m p le

m at h

re la ti o n s (e .g .,

a + 5 = 8 )

T es ta b le

th eo ri es

in ac ti o n

R ep re se n ta ti o n o f

co m p le x re al it ie s

L o g ic al

n ec es si ty

D if fe re n ti at io n b et w ee n

co g n it iv e fu n ct io n s

(m em

o ry

v s. at te n ti o n )

11 – 1 2

F le x ib le

lo g ic al

m u lt ip li ca ti o n

P ro p o rt io n al

re as o n in g .

C o o rd in at io n o f

sy m b o li c st ru ct u re s

S u p p o si ti o n s,

is o la ti o n o f

v ar ia b le s

Im ag in at io n o f

th e n o n re al

L o g ic al

v al id it y

o f p ro p o si ti o n s

D if fe re n ti at io n b et w ee n

cl ea rl y d if fe re n t

d o m ai n s (s p ac e

v s. m at h s)

1 3 – 1 4

S tr at eg ic

cl as si fi ca ti o n

in cl u d in g re le v an t–

ir re le v an t in fo rm

at io n

A lg eb ra ic

re as o n in g

b as ed

o n m u tu al ly

sp ec if ie d sy m b o l

sy st em

s

H y p o th es is d ri v en

ex p er im

en ta ti o n

O ri g in al it y in

m en ta l im

ag es

G ra sp

o f fo rm

al re la ti o n s

A w ar en es s o f sp ec ia li ze d

m en ta l o p er at io n s

w it h in

a d o m ai n

1 5 – 1 6

M u lt il ev el

cl as se s.

N et w o rk s o f

cl as si fi ca ti o n cr it er ia

G en er al iz ed

co n ce p t

o f v ar ia b le

In te g ra te d th eo ry

b u il d in g

P er so n al

im ag in al

w o rl d s, ae st h et ic

cr it er ia

R ea so n in g o n

re as o n in g

In te g ra te d co g n it iv e

th eo ry

606 Educ Psychol Rev (2011) 23:601–663

T a b le

3 S u m m ar y o f p ri n ci p le s fo r ed u ca ti n g th e d ev el o p in g m in d

W o rk in g m em

o ry

C o n ce p tu al

ch an g e

R ea so n in g

L ea rn in g to

le ar n

C ri ti ca l th in k in g

M o n it o r o n g o in g

p er fo rm

an ce .

M o d el

te m p la te s fo r th e b as ic

m en ta l o p er at io n s in

ea ch

S S S , su ch

as cl as si fi ca ti o n

(c at eg o ri ca l) , m o d el s fo r ca u sa l

re la ti o n s (c au sa li ty ), fo r m en ta l

n u m b er

li n e o r ar it h m et ic

o p er at io n s (q u an ti ty ), fo r

v is u al iz at io n o r sp at ia l

co o rd in at es

(s p ac e) .

D ec o n te x tu al iz e in fe re n ce

b y

in te rp re ti n g th e p re m is es

an al y ti ca ll y an d d if fe re n ti at in g

th em

fr o m

th ei r co n te n t an d th e

co n cl u si o n (u se

k ey

el em

en ts

su ch

as th e co n n ec ti v es ).

D if fe re n ti at e b et w ee n :

Id en ti fy

ce n tr al

is su es

an d as su m p ti o n s.

K ee p tr ac k o f th e fl o w

o f

in fo rm

at io n an d u p d at e

(r eh ea rs e)

it fo r as

lo n g

as n ee d ed .

M o d el

te m p la te s fo r co n ce p tu al

ch an g e,

su ch

as b ri d g in g ,

fu si n g , d if fe re n ti at in g ,

an d el im

in at in g C o n ce p ts .

D if fe re n ti at e b et w ee n ty p es

o f

re as o n in g (e .g ., d ed u ct iv e v s.

in d u ct iv e)

an d lo g ic al

fo rm

s (e .g ., d is ju n ct io n ,

co n ju n ct io n , et c. ).

L ea rn in g as

(1 ) k n o w le d g e

ac q u is it io n , (2 ) co n ce p tu al

ch an g e,

an d (3 ) ac q u is it io n

o f p ro b le m

so lv in g sk il ls .

E n v is ag e al te rn at iv e m o d el s

an d as so ci at e ea ch

w it h

su p p o rt iv e ev id en ce , lo g ic al

su b st an ti at io n , co n ce p tu al

o r b el ie f sy st em

s. M an ag e m o re

th an

o n e

ta sk

si m u lt an eo u sl y

(e .g ., b y al te rn at in g

b et w ee n b lo ck s o r ty p es

o f in fo rm

at io n , if n ee d ed ).

M et ar ep re se n t n ew

co n ce p ts

an d sy m b o li ze .

D if fe re n ti at e b et w ee n lo g ic al ly

n ec es sa ry

an d li k el y co n cl u si o n s.

R es is t ce rt ai n ty

ab o u t co n cl u si o n s

u n le ss

re al ly

lo g ic al ly

n ec es sa ry .

K n o w le d g e (1 ) ac q u is it io n

(o b se rv at io n , as k in g ,

re ad in g , et c. ), (2 ) re te n ti o n

(r eh ea rs al , as so ci at io n ), an d

(3 ) ev al u at io n (e x p er im

en t,

lo g ic al

v al id at io n ).

S ta y o pe n -m

in de d, sk ep ti ca l

an d qu es ti on in g . T ol er at e

am bi gu it y. P ro te ct ju dg m en t

fr om

yo u r ow

n bi as es .

B in d it em

s ac co rd in g to

ty p e

an d ti m e o f p re se n ta ti o n .

S p o t co n tr ad ic ti o n s b et w ee n

p re m is es

an d co n cl u si o n s

an d co n st ru ct

al te rn at iv e m o d el s

fo r p re m is es

an d co n cl u si o n s.

M en ta l o p er at io n s in

ea ch

S S S as

k n o w le d g e

ex tr ac ti o n m ec h an is m s.

A d o p t an

in fo rm

ed p re fe re n ce

b as ed

o n ev id en ce , ar g u m en t,

an d p er sp ec ti v e.

In h ib it ir re le v an t te m s.

W it h h o ld

ju d g m en t u n ti l to

ex am

in e al l re la ti o n s in v o lv ed .

P ro ce ss in g d em

an d s o f

(1 ) m en ta l o p er at io n s

(e .g ., m en ta l ro ta ti o n

v s. ar it h m et ic

o p er at io n s) , (2 )

re p re se n ta ti o n s (e .g ., im

ag es

v s.

n u m b er s) , (3 ) in fo rm

at io n

ty p e,

o rg an iz at io n o r v o lu m e.

R ev is e ap p ro ac h o r p er sp ec ti v e,

if ap p ro p ri at e.

M et ar ep re se n t n ew

re as o n in g p at te rn s

an d sy m b o li ze .

A ct io n s re g u la ti n g p ro ce ss in g

(e .g ., st im

u lu s en g ag em

en t,

ig n o ri n g d is tr ac to rs , re fl ec ti o n )

R ef le ct

o n th e n at u re

o f

k n o w le d g e (w

h at

is tr u th ,

w h at

is ev id en ce , w h at

is re as o n o r ca u se , w h at

is m o re

im p o rt an t, re as o n o r ev id en ce ).

O n e’ s o w n st re n g th s an d

w ea k n es se s in

d if fe re n t

d o m ai n s an d p ro ce ss es .

M et ar ep re se n t n ew

d ec is io n s

an d sy m b o li ze .

Educ Psychol Rev (2011) 23:601–663 607

Emergence of representations

All systems are present at birth. There is strong evidence that the core processes of categorical (Butterworth 1998; Carey 2009), quantitative (Dehaene 1997; Piazza 2010; Sloutsky and Fisher 2011), causal (Haun et al. 2010; Keil 2011), spatial (Haun et al. 2010), and social (Molenbergs et al. 2009; Rizzolatti and Craighero 2004) thought are present in the first weeks of life. The engagement of core operators with their respective aspects of the environment generates representations that provide the behavioral or mental analogues of the basic laws of physics, biology, and psychology. These enable the infant to interact with increasing efficiency with physical objects, animals, and other humans. Therefore, the development in the first 2 years of life suggests, contrary to Piaget, that this period of life, far from being simply sensorimotor, is richly conceptual (Carey 2009; Gelman 2003; Mandler 2004). According to Carey (2009), the major domains of infant cognitive development are objects (the physical world), agency (the psychological world), and number. These are envelop categories which include concepts and categories about the identity, the spatial, and the causal relations of objects; the active self which acts as a causal agent for objects and other persons generating self-knowledge and knowledge about others (agency); and the quantitative relations between objects and beings.

However, thought at this period is constrained by limited concentration and floating attention that is easily attracted by sensory variation in the environment. These limitations, together with the lack of language, hinder drastically the integration of experience. As a result, the infant’s conceptual world is incoherent and content- and context-bound. It needs to be stressed, however, that what appears as a limitation from one point of view is an advantage from another. Specifically, floating attention and ensuing superficial processing of information about the world enables the infant to generate a rich pool of experience and knowledge about the world at a period of universal inexperience and ignorance that will be pruned, elaborated, differentiated, and integrated in the following phases.

Differentiation of representations

The gradual emergence of language during the second year of life brings representations as such into focus so that they can be reflected upon and elaborated. The explosion of the “why” questions at this phase (Brown 1968; McCune 2008) is indicative of a reorientation of the infants’ knowing priorities from the world itself to their own representations about the world. In a sense, the infants appear to realize, though not explicitly, that it is time to work on the representations already constructed.

Two-year-olds represent, but they cannot metarepresent. DeLoache and Burns (1994) showed a dramatic developmental change between 24 and 30 months of age in this regard. Specifically, 24-month-olds could not use a picture to retrieve an object whereas 30-month-olds could. Therefore, in the middle of the third year of life, children start to be able to view pictures as representations of current reality. In line with these findings, Dalke (1998) showed that 3-year-olds have some ability to make and use maps as a guide for their actions (e.g., where to look for an object). Huttenlocher et al. (2008) showed that this ability develops gradually from less (e.g., placing an object in actual place following a map at the age of 3 years) to more complex tasks (e.g., retrieving an object following a map at the age of 4 years).

Thus, at about the age of 3–4 years, children start to differentiate representations from each other and from the objects they represent. For example, they can see a toy car as a representation of real cars and as an object in itself. DeLoache (1995, 2000) called this

608 Educ Psychol Rev (2011) 23:601–663

emerging differentiation of representations dual representation. Dual representations enable children to reflect on and operate explicitly on representations as tokens of reality. Thus, they represent a major milestone in development because they open the way for the intentional use of symbols and symbol systems, such as writing.

As a result, preschoolers start to build concepts in the various environment-oriented domains. There must be at least two representations to conceive of a class (e.g., “fruit”), a quantity (e.g., more or less), a cause−effect relation, or a spatial relation (e.g., “above”; Case 1985; Demetriou 1998; Fischer 1980). Moreover, they start to understand the multifaceted nature of the world, as indicated by their understanding of the appearance−reality distinction, because they can represent an object in its present and in an earlier condition (Flavell et al. 1995), and their acquisition of a theory of mind, because they can understand that the same thing can be represented differently by two persons (Wellman 1990).

Integration of representations

Another important milestone comes at about the age of 7–8 years. With practice and increasing awareness about representations (Demetriou and Kazi 2006; Flavell et al. 1995), children begin to realize that representations constrain each other. For example, words in a sentence constrain how other words will be used and sentences constrain the meaning of the sentences to follow (Brown 1968; McCune 2008). This is the beginning of the grasp of logical necessity (Miller et al. 2000; Moshman 1990, 1994). With logical necessity as a reference criterion for the validity of the relations between representations, children can start to systematically build alternative mental models for concepts and relations (Ricco 2010) and to represent the underlying relations between concepts or mental operations. For example, in the domain of categorical thought, systematic classification starts to be possible because a criterion may be preselected and applied in order to specify the identity of objects and their similarity relations (e.g., color, shape, etc.). This criterion may be used to classify things on the basis of having or not having the attributes specified by the criterion. Thus, the logical relation of class inclusion can be grasped, indicating that semantic networks are bound by obligatory relations.

Conditional representations

A third milestone comes at the beginning of adolescence, when adolescents become able to construct conditional representations. Conditional representations enable the thinker to view systems of representations from the point of view of each other. Technically speaking, they can integrate at least two dimensions with at least two levels each (e.g., they grasp proportionality and they formulate alternative hypotheses which they can test with especially designed experiments).

The meaning of each component representation in conditional representations emerges from the relations of this representation with all other representations in the system. Consistency and coherence in these representations gears on the relations between the relations involved rather than in the relations between representations and the objects represented (e.g., in the proportional relation 2/4:3/x, x is derived from the relations rather than from the numbers themselves). Logical necessity is fully established at this phase and acquires its full use as a gauge of the fit of each representation or variation in the system. In the previous phase, logical necessity is a tool for evaluating consistency within concepts. Now it is a tool for guiding the exploration of the relations across concepts. Therefore, empirically invalid or counterintuitive representations are acceptable if they can be derived

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from the logical relations involved. As a result, each representation in a system of conditional representations is a point of variation, given the relations involved. Thus, new representations can be introduced and the implications examined from the point of view of all other representations, given the relations that bind them. This results in the transformation of thought from being descriptive and organizational to suppositional and foresighted (Demetriou et al. 1993; Demetriou and Kyriakides 2006).

Differentiation of thought from knowledge

The next milestone comes at about the end of secondary school or in the early 20s. Young adults differentiate (1) between thought processes vis-à-vis their use for different aspects of knowledge acquisition and problem solving and (2) between thought, as a system of processing, and knowledge, as information about the world. That is, they understand that different knowledge or belief systems governing the operation of social and cultural groups are constrained by rules related to the values of these groups rather than from cognitive or inferential processes as such. It is understood that conclusions or interpretations may vary across systems even when the same inferential or thought processes are used. Therefore, knowledge or belief systems may be compared, integrated, or used separately according to goals or needs (Demetriou and Bakracevic 2009; Demetriou and Kazi 2001, 2006; Reynolds et al. 2005; Schaie 1996).

These changes interweave with the social life of the adult which requires the flexibility that is necessary to consider and negotiate the multiple perspectives that are usually associated with different individuals, groups, or institutions adults normally interact with. Thus, on the one hand, relativistic and dialectical thinking (Basseches 1984; King and Kitchener 1994; Riegel 1973) emerges from the flexibility that is associated with the multiplicity of perspectives that the social environment forces onto the adult. On the other hand, the handling of this very reality is conducive to the elaboration of a principled approach to problems and situations that would give coherence to meaning making, life choices, and orientations. That is, despite the recognition of the relativity of perspectives or belief systems, and the respect that may be accorded to each, persons have to choose one of them and structure and justify their actions or choices accordingly. Eventually, judgment and decision making may be elevated to wisdom. Wisdom generates integrative and balanced judgments and decisions that are rational rather than logical, oriented to the future rather than the present, and to the best interests of many rather than one or few persons (Baltes and Staudinger 2000; Demetriou and Bakracevic 2009; Stanovich 2009).

The architecture of the representational capacity system

Mental functioning at any moment occurs under the constraints of the available representational capacity. Representational capacity is the maximum amount of information chunks or units (e.g., mental images, words, and numbers) and mental operations (e.g., mental rotation, grammatical rules, and arithmetic operations) that the mind can efficiently activate simultaneously. Representational capacity is the work space of the mind. That is, it is the mental field where information is operated upon, analyzed, connected, combined, and transformed for the sake of interpretation, inference drawing, reaching conclusions, formation of action plans, and problem solving (Baddeley 1990; Cowan 2010; Engel de Abreu et al. 2010). In psychometric studies, representational capacity always emerges as a separate block of factors (Demetriou et al. 1993, 2002, 2005, 2008; Engel de Abreu et al. 2010; Hornung et al. 2011; Unsworth and Spillers 2010). The content of this field is

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transparent to awareness and thus amenable to interaction with the processes of control and consciousness, to be described below.

There is general agreement that representational capacity involves (1) modality-specific components that hold information for a very short period of time, (2) a short-term storage that holds a part of information that is available in modality-specific components for further processing according to a goal, and (3) an executive component that involves processes applied on the information represented in short-term storage for the sake of the goal (Baddeley 1990; Hornung et al. 2011; Tillman et al. 2008). Wood (2011) showed that there are storage systems for spatiotemporal information related to the movement of objects in space, for object identity information related to the retention of the characteristics of objects, and for snapshot information related to the location of objects. It is highly interesting that these storage systems relate closely to the core processes in the SSS. The first system is related to spatiotemporal processes for tracking objects in the spatial SSS, and it may also be related to the core processes for representing causal relations. The second is clearly related to the construction of concepts in the categorical SSS. The third system is an important component of the spatial SSS, and it may be related to subitization processes in the quantitative system.

Therefore, the architecture of specialized storage capacity reflects the strong domain- specific origin of mental processing. The integrative role of this space that can put together information coming either from the same or from different specialized domains is reflected in Baddeley’s (2000) episodic buffer; it emanates from the interaction between representational capacity and both inferential and control processes, to be discussed below.

The development of representational capacity

The developmental profile of working memory varies in different research paradigms depending upon the combination of components measured. However, there is general agreement on the following:

First, iconic memory capacity approaches adult levels very early in life. Specifically, Blaser and Kaldy (2010) showed that iconic memory has a capacity of five objects by the age of 6 months, which is very close to the six-object capacity of adults. Moreover, Zosh et al. (2011) showed that 9-month-old infants, like adults, can represent in working memory up to three ensembles of information, each of which contains summary information for many more than three objects at a time. The processing of information within the ensembles is possible after the age of 2–3 years according to the development patterns summarized below. This finding is important in suggesting that infants do posses the visual memory space that is required for the operation of the core processes discussed above. Thus, representations seem to emerge from the contents of sensory memory and held as undifferentiated blocks. Their differentiation begins during the third year of life, and it opens the way for the elaboration of their relations as discussed above.

Second, until the end of the second year of life, children can focus on and represent in short-term storage only one unit of the information held in iconic storage. This implies that some control of attention becomes possible around the age of 2 years that enables infants to focus processing on a small part of the content of their sensory storage. From the age of 2 years onwards, when executive operations are held to a minimum, short-term storage capacity is two, three, and four units at the age of 3–4, 5–6, and 10–11 years, respectively (Case 1985; Cowan 2010; Halford 1993; Demetriou et al. 1993, 2002). Pascual-Leone (1970) maintained that it continues to increase until it reaches the limit of seven units at the age of 15–17 years.

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Third, at any age, the exact capacity of working memory varies considerably depending upon the volume of executive processes needed. Overall, Barouillet et al. (2011) showed that the short-term memory span for a given type of information is a function of the total storage possible for this type of information and the cognitive load induced by processing, which is itself related to the time devoted to them: With increasing load, the storage decreases. However, executive processes do not only have a cost for storage capacity. They are also beneficial to it. Camos and Barrouillet (2011) showed that 7-year-old children were able to shift attention from processing in one set of information to rehearse the information in another set that they would have to recall later and come back again. Six-year-olds did not demonstrate this flexibility. This is due to the fact that between the ages 7 and 9 years, children acquire the cognitive flexibility needed to switch attention between different cognitive processes, such as rehearsal needed for the maintenance of information in short- term storage and the processing of information. Davidson et al. (2006) showed that flexibility in switching between goals during processing develops throughout adolescence and that adults are more adept than adolescents to regulate their rate of processing to ensure accuracy. Therefore, with development, accurate self-monitoring and flexible executive control processes compensate for the mental space required to represent them.

Fourth, the type of information and related experience are also important. For example, in the spatial domain, the capacity varies from three units at the age of 6 years to five units at the age of 12 years. In the domain of mathematical thought, it varies from about two to about four units in the same age period. Demetriou et al. (2005) showed that extensive training in a particular modality, such as the massive training that is required to learn the Chinese logographic system of reading and writing, may have a highly beneficial effect on visuospatial working memory that does not generalize to phonological storage or to the representation of executive operations. This study showed that the capacity of visuospatial short-term storage of Chinese children exceeded the corresponding capacity of Greek children by about three units of information throughout the period from 8 to 14 years of age, whereas their working memory for words and numbers was the same.

In conclusion, these variations reflect the fact that different types of information, related to the various SSS, put different demands on the available mental resources. The executive operations required for control and processing compete with SSS-specific information for available resources. Training and familiarity facilitate short-term storage because they diminish the demands of executive processes due to the utilization of the accumulated experience to novel situations and to the automatization of procedures through practice. These findings are consistent with the findings of Ericsson and Kintsch (1995) which suggest that the effective use of working memory depends upon the coding schemes available in long-term memory. For instance, the inclusive concept “energy,” once in working memory, alerts a large number of component concepts in long-term memory if the person has these schemas. Working memory is largely a holding system for pointers to schemas in long-term memory. Therefore, developmental differences between adult experts and younger persons may be explained, partly, by the fact that adults posses more efficient and informationally richer schemas than younger persons do. Moreover, the differences in developmental rates across domains may, in part, be explained by the relative differences in the experience in different domains. Education is of course crucial in this regard.

The architecture of the inference system

Inference involves processes enabling the transfer of meaning from one representation to another. Transfer normally occurs on the basis of properties which are present in both the

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initial (or source) representation and the target representation. In inference, these common properties are used as an intermediary between the two representations such that properties characterizing the initial representation (apart from their common properties) are also ascribed to the target representation.

There are several types of reasoning. Inductive, analogical, (Holland et al. (1986), and deductive reasoning (Moshman 1994; Ricco 2010; Rips 1994) are the three types that have been studied extensively in both logic and psychology. Several studies have shown that these types of reasoning are based on different inferential mechanisms (Heit and Rotello 2010), and they emerge as separate constructs in structural models of reasoning. Moreover, they are related to each other by common inferential processes which emerge as a separate level in hierarchical models of cognition that involves several of the SSS summarized above (Demetriou and Efklides 1994; Demetriou et al. 2008, 2010a, b; Mouyi 2008).

The development of inference

Induction and analogy

Students of infancy converge on the assumption that the core systems allow generalizations that enable infants to interpret new encounters on the basis of their similarity with available concepts (Carey 2009; Gelman 2003; Mandler 2004). Thus, inductive inference is part of the very organization and functioning of the mental architecture of the mind probably from birth.

Initially, inference is based on perceptual similarity, which is the most readily apparent form of similarity. In fact, the very methods used to study infants’ cognitive abilities, such as the habituation method (decline of interest in a sequence of stimuli, such as a particular shape or image of an object, when they are similar to each other; see Butterworth 1998), assume that infants are sensitive to “same” and “different” relations (Tyrrel et al. 1991). This ability enables infants to construct categories by extracting general characteristics from overlapping perceptual features that appear across objects and by selectively attending to relevant features while ignoring irrelevant ones based on available core concepts that direct feature search and interpretation (Sloutsky and Fisher 2011).

In a similar vein, analogical reasoning transfers meaning on the basis of relations between objects or concepts rather than individual objects or representations. Wagner et al. (1981) showed that infants as young as 9 months of age preferred to look at an arrow pointing up when hearing an assenting tone and at an arrow pointing down when hearing a descending tone. In line with this evidence, recent studies of intuitive statistics showed that 8-month-olds make first-order generalizations about the composition of a population on the basis of exemplars of this population (Xu and Garcia 2008). Impressive as it may sound, 9-month-old infants seem able for second-order generalizations or metageneralizations. Dewar and Xu (2010) showed that 9-month-olds were able to abstract a first-order relation and thus capture the pattern underlying the characteristics of objects in different boxes (i.e., all round or all square or all triangular) and generalize to a second-order relation that generates expectations about new objects (i.e., all objects in a box are similar in shape, so if there is a starlike object in a new box all objects in this box must be starlike). Gelman and Coley (1990) showed that 30-month-old children use perceptual similarity as a basis for drawing inductive inferences. Interestingly, Williamson et al. (2010) have recently shown that 3-year-olds can abstract and imitate rules underlying behavior, in addition to imitating the behaviors they observe as such.

Mouyi (2008) (Demetriou et al. 2010a, b) showed, in a very detailed study of reasoning, that inductive reasoning develops in three main levels from the age of 6 to the age of 12 years. At the first level, children can identify patterns and formulate generalizations on the basis of a

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single dimension or relation. However, at this level, the child’s experiences and knowledge may bias inference to conclusions inconsistent with the generalizations suggested by patterns. Therefore, control processes are not yet powerful enough to protect inference from privileged knowledge and experiences. At the second level, inductive reasoning can handle hidden or implied relations that require from the thinker to combine information present to the senses with knowledge stored in long-term memory. Mapping out the implied relation requires that non-relevant information in the premises or in long-term memory are inhibited. Moreover, inductive inferences based on the syntactical components of verbal premises can be drawn. Negative premises may be manipulated at this level. Thus, it is suggested that control processes at this level are powerful enough to direct the inferential process on target and protect representational capacity from overloading with irrelevant information. Finally, at the third level, inductive reasoning is based on theoretical supposition. That is, possibilities can be specified in advance and information in the premises is analyzed in reference to them. As a result, multiple parameters and relations can be simultaneously considered and manipulated. Generalizations can therefore be extracted from the relations and elevated to mental models.

Later on, analogical reasoning can structure third- or higher-order relationships involving abstract relations such as “parents are to family what teachers are to education,” which require a rich culturally relevant knowledge (Demetriou and Kyriakides 2006).

From probabilistic to deductive reasoning

There are signs of pure reasoning that is based on probabilistic inference very early in life. Téglás et al. (2011) showed that infants can make correct predictions about the future state of an event (e.g., which of several objects will exit an opening) based on several parameters (such as each objects’ frequency in the set, distance from the opening, and time available). According to Téglás et al. (2011), 1-year-old infants have “the ability to generate physically plausible candidates for future world states, consistent with the observed present” (p. 1058).

However impressive this finding is, there is nowadays general agreement that deductive reasoning as such does not appear before representations are differentiated from each other and expressed into natural language. Moreover, in all models of reasoning, the development of deductive reasoning is associated with awareness of cognitive processes and cognitive control. These enable the thinker to search systematically for and envisage the relations suggested by the premises of an argument and their relations; resist belief biases, if the content of the premises and their relations do not converge to the same conclusion; and gear on the reasoning process so that the logical conclusion overrides the conclusion suggested by knowledge and belief biases.

This interpretation is consistent with the dual-process theory of reasoning and reasoning development. “Dual-process accounts postulate the operation of a central domain general system (often termed “System 2”), characterized as “analytic” and relatively content free in nature, and a peripheral domain-specific system (often termed “System 1”), which is more “heuristic, modular and highly context dependent” (Ricco and Overton 2011), and hence drawing upon the SSS-specific core processes. In terms of the theory proposed here, system 2 reasoning emerges from the operation of system 1 reasoning through awareness of similarities and differences between heuristic processes and the construction of representa- tions that unify and represent these heuristic processes. That is, the key components of reasoning are gradually decoupled from their context, fused with each other, and metarepresented as logically necessary reasoning schemes which are gradually interconnected with each other as reasoning rules. This is the system 2 algorithmic reasoning (Ricco 2010; Overton 2010). In line with this model, Heit and Rotello (2010) showed that inductive reasoning is based on a more automatic mechanism that uses

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similarity as a basis for inferring a relation and deductive reasoning is based on analytical processes that draw on validity. Moreover, they showed that even deductive reasoning may shift to similarity for inference drawing, if forced to operate under time constraints that do not allow the activation of the analytical processes required for the evaluation of validity.

Moshman’s (1990, 1994) and Mouyi’s (2008) levels of deductive reasoning development explicate the gradual emergence of system 2 reasoning from system 1 reasoning. During the preschool years, children reason logically using inferences, but they do not think about inference. Children correctly use most of the connectives and conditionals involved in inference schemas, but they have no understanding of the inferential process and are not aware that the premises constrain the conclusion (e.g., “there is a cat; there is an apple; so, there is a cat and an apple).

This awareness appears for the first time by about the age of 5 or 6 years, when inference becomes explicit but logic is implicit. Children at this stage are explicitly aware of the inferential process that connects premises and conclusions into coherent arguments and are sensitive to logical necessity. Thus, they can grasp modus ponens arguments (e.g., “if there is a either a cow or a goat then there is a pear; there is a cow; therefore there is a pear”). However, logic as such is still implicit in their reasoning and does not function as a frame to explicitly guide reasoning. Thus, they fail on problems in which the logical form of the argument must be explicitly differentiated from its content (e.g., birds fly; elephants are birds; therefore, elephants fly). At the end of this phase, at the age of about 9–10 years, children can deal with modus tollens inferences (e.g., “if there is a cow there is an apple; there is no cow; therefore, there is no apple”). In comparison to modus ponens, modus tollens requires a model construction process which takes the modus ponens argument as a basis and then constructs alternative models which are compared with each other. Overall, reasoning at this level involves more steps and more operations (Johnson-Laird 2001).

At 11–12 years of age, logic becomes explicit, but metalogic is still implicit. Preadolescents understand that “an argument is valid if, regardless of the empirical truth of its premises and conclusions, has a logical form such that, if the premises were true, the conclusion would have to be true as well” (Moshman 1990, p. 212). Thus, at this phase, they can deal with simple denying the antecedent and affirming the consequent fallacies (e.g., “if there is a cow there is a pear; there is a pear; we cannot be sure if there is a cow”). Fallacies place high demands on the system. Many alternatives must be retrieved from memory and processed (Barrouillet et al. 2000). Moreover, the very nature of the outcome of processing is peculiar because the conclusion is that no conclusion can be reached. Therefore, the thinker at this level must accept that not all arguments are determinate and thus uncertainty may be part of the reasoning process itself. Finally, at the stage of explicit metalogic, adolescents can function as theorists of reasoning. As a result, they can specify all implications of an argument, determinate or indeterminate, based on all possible logical forms involved.

The architecture of the consciousness system

Humans usually operate under conditions of uncertainty caused by novel, conflicting, or incongruent information relative to a specific goal. Thus, to meet their goals, humans must be able to focus attention and process goal-relevant information efficiently, filtering out goal-irrelevant information, and assemble action. The core mechanisms of consciousness involve monitoring processes ensuring (1) awareness of the current goal, (2) an evaluative function which regularly compares the present state with the goal, and (3) a control function which registers discrepancies between the present state and the goal and suggests corrective actions (Demetriou 2000). The core mechanisms of self-regulation and executive control

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involve a process of (1) selection by inhibition (Leslie et al. 2004) and (2) a regulatory process that enables the individual to adjust mental or overt action to goals (Gibson and Pick 2003; Zelazo et al. 2003, 2005). It needs to be noted, however, that not all cognitive functioning ever reaches consciousness. In fact, consciousness is needed when no ready-made or automated schemes of action are available.

Thus, the input to consciousness is information arising from the functioning of all other systems. It operates as one of the main mechanisms for the integration of otherwise separate and independent mental constructs or brain networks (Baars 2002) and, eventually, for cognitive and behavioral control and flexibility (Cleeremans 2008). In its integrative role, consciousness is the seat of subjectivity and self because it unites one’s own known past, present, and intended future in a single line of experience. Thus, at one and the same time, it is a causal factor for one’s own action and a source of attributions and explanations for the action of other persons (Printz 2003).

Metarepresentation is the generative aspect of consciousness. That is, metarepresentation is an ideoplastic process that looks for, encodes, and typifies similarities between mental experiences (past or present) and between representations to enhance understanding and problem-solving efficiency. As a result of this process, new mental operations, new higher- order rules integrating different operations, and new representations to stand for these new operations and rules are created. We will explicate this process below in reference to the development of reasoning.

Also, metarepresentation gradually builds maps of mental functions which are continuously updated. These maps are generally accurate representations of the actual organization of cognitive processes in the domains mentioned above. Research suggests that the SSS, the components of working memory, and inference emerge from self- evaluations of performance on tasks as they emerge when we analyze actual performance on the same tasks. This finding indicates that working on the tasks generates experiences that reflect performance on them and that these experiences are recorded and recovered as such (Demetriou and Efklides 1989; Demetriou et al. 1993; Demetriou and Kazi 2001, 2006). Thus, when needed, these maps may be called upon to guide problem solving and understanding (Demetriou and Bakracevic 2009). Admittedly, there are large individual differences in the accuracy of these maps (Demetriou and Kazi 2001). Metarepresentation reminds one of Piaget’s (2001) reflective abstraction and Karmiloff-Smith’s (1992) representational redescription. Like reflective abstraction, it abstracts general patterns from different mental functions or activities. Like representational redescription, it reorganizes them at a higher, more efficient representational level.

It is important to note that consciousness is also the link between mind and personality (Demetriou et al. 2003). On the one hand, it integrates self-representations and self- evaluations of cognitive processes with personality dispositions underlying attitudes to learning and problem solving, such as being organized and systematic or adventurous and open to experience. On the other hand, it brings personality and emotional influences into cognitive functioning through the moderating effects that the emotional outcomes of cognitive processing, such as failure-based avoidance, may have. This integrative role is expressed through a personal constant that moderates mental, emotional, and personality experiences and dispositions. “That is, the individual adopts a particular attitude to his or her performance and ability which is consistently applied across different domains. This constant is used to adjust any signals regarding his or her functioning to a level which is personally characteristic” (Demetriou and Kazi 2001, p. 117). This mechanism explains why individuals are consistently accurate, lenient, or strict in their self-evaluations. Interestingly, this mechanism is recognizable by others, such as parents and teachers, and it

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moderates even interpersonal interactions. We will see below that this mechanism must be of concern to education because it influences the students’ choices and attitudes to learning.

The development of consciousness

All aspects of consciousness develop systematically from birth to maturity (Demetriou and Kazi 2001, 2006; Demetriou and Bakracevic 2009; Zelazo 2004), improving the quality of subjective experience, the resolution and refinement of mental processing, and executive control (Baars and Franklin 2003). In the first year of life, there is body–action consciousness that involves implicit awareness of one’s own actions and bodily sensations (Kopp 2011). This is minimal consciousness that is intertwined with the emergence of representations from the core operators of infancy. In the second year of life, body–action consciousness is embedded into self-consciousness that involves self-recognition in the mirror and self-reference through language or pointing. This enables infants to consider their capabilities vis-à-vis a situation and to execute means–end actions. Using the dimensional change card sort task, Zelazo et al. (2003) showed that 2-year-old children can operate with only a single rule (e.g., “If it is red, it goes here”). In the third year of life, infants become capable of reflective consciousness. As a result, by the age of 3, children can operate with a contrastive relation (e.g., “If it is red, it goes here; if it is blue, it goes here”), which indicates reflective planning of action. At the age of 5 years, children can shift from one rule to another rule. According to Zelazo and Frye (1998), to be able to shift from the one rule to the other, the child must be able to integrate the rules into a higher- order rule that specifies when each of the two lower-order rules is to be used and be aware of them. For instance, in the example of dimensional change card sort described above, this rule might be as follows: “If we were playing color, then if it is red it goes here and if it is blue it goes here, but if we were playing shape, then if it is a red car it goes here and if it is a blue flower it goes here”).

The development of awareness of cognitive processes is more complicated and lags behind awareness of action and executive control. Preschoolers understand that thinking is an internal mental activity referring to objects or events (Flavell et al. 1995, 1997). Moreover, they are aware of their own actions and can intentionally modify them as they evolve (Demetriou 2000). One of our studies (Demetriou and Kazi 2006, study 1) examined whether 3- to 7-year-old children are aware of the cognitive processes involved in tasks addressed to three domains of reasoning—that is, spatial, quantitative, and categorical reasoning. Specifically, children were asked to judge whether pairs of persons doing various tasks with various objects (e.g., both classifying or both counting or the one classifying and the other counting) were thinking of the same thing. We found that at the age of 3–5 years, the majority of children based their judgements on the perceptual similarity of the objects involved rather than on what the persons were required to do with them. At the age of 6 years, children were able to recognize that pairs of tasks belonging to a different domain require different mental processes. However, it was only at the age of 7 years that the majority of children were able to recognize the mental operation of pairs of tasks where the same operation, such as classification or counting, was applied on different objects.

In another study, 11- to 17-year-old adolescents were examined by tasks addressed to four domains: quantitative reasoning, causal reasoning, social reasoning, and drawing. In addition to solving the various tasks addressed to each of these domains, participants were asked to evaluate their performance on the tasks and to answer a general self-representation inventory probing their general self-concept related to each of the domains (e.g., “I can easily derive the mathematical rules behind many specific examples”, “To find out which of

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my guesses is correct, I proceed to methodically consider each time only the things my guess proposes”, “I understand easily the intentions of others before they express them”). It was found that the relation of self-evaluation with reasoning was very weak at the age of 11 years and then steadily and systematically increased until it became very strong (0.97) at the age of 15–16 years. Interestingly, the relation of self-representation and reasoning did follow the same trend, but with a considerable age lag. It was very low until the age of 13, it rose to moderate at the age of 14 (0.34), and to high (0.55) at the age of 15–16 years. In a similar fashion, the relation of self-representation and self-evaluation was very low until the age of 13 years, rose to moderate at the age of 14 (−0.30), and to very high at the age of 15– 16 years (−0.80). The negative relation here implies that with increasing accuracy in self- evaluations, adolescents become more conservative and strict in their self-representation (Demetriou and Kazi 2001, 2006, study 2).

The findings above suggest that self-awareness and self-evaluation of cognitive processes develop in a recycling fashion, which involves four major cycles: 0–2, 3–7, 8–12, and 13–18 years of age. Within each phase of development, self-evaluation and self-awareness concerning the representations and relevant mental operations are very low and inaccurate at the beginning, and they tend to increase and to become more accurate with development until the end of the phase. Entering the next phase resets both of them to an initial low level, from where they gradually take off again with the development of the new phase-specific problem-solving operations and skills. This pattern of change indicates that increasing self-awareness of cognitive processes becomes part of the very functioning of the processes concerned. We will try to show below that this intertwining of cognitive functioning with awareness about the cognitive processes enables the explicit representation of cognitive processes and their restructuring into new, more inclusive, and flexible representations. This is metarepresentation. There is accruing evidence that self-awareness and executive control are part of the learning process and that the efficiency of learning changes in development because of changes in these processes (Kuhn and Pease 2006).

Structural, Functional, and Developmental Relations Between Systems and Processes

In this section, we will review research that substantiates the architecture of mind proposed by this theory and reveals the dynamic relations between the various structures and processes during development. The emphasis of presentation is to highlight the relative importance of domain-general and domain-specific processes in intellectual development.

Relations among structures and functions

Figure 2 shows an idealized model that translates the architecture of mind proposed here into the conventions of structural equations modeling of cognitive abilities. It can be seen that this model includes four blocks of factors that correspond to the four major systems of processes involved in the proposed architecture of mind. The relations between systems are also represented. Specifically, the model involves factors that stand for (1) different mental operations within each of the five SSS, one factor for each SSS, and a higher-order factor that stands for general problem-solving ability; (2) different components of working memory and executive control and a higher-order factor that stands for representational capacity; (3) self-evaluations and self-representations of different mental operations within each of the five SSS, one factor for general self-concept in concern to each SSS, one factor for metarepresentation, and a higher-order factor that stands for general self-awareness and

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self-representation; and (4) factors that stand for different processes within inductive and deductive reasoning and a general factor that stands for general inferential processes. No study has examined this model in its entirety. However, several studies substantiated models which are close approximations to this model (Demetriou and Efklides 1989; Demetriou et al. 1993, 2008; Demetriou and Kazi 2001, 2006).

This is an all-encompassing model that incorporates several local architectures suggested by psychometric research. A broad and generally accepted architecture in psychometric psychology stipulates that cognitive abilities are hierarchically organized, including narrow domain-specific abilities at a first level of organization; several broad abilities at a second level, which frame and coordinate the functioning of the narrow abilities; and, finally, general intelligence (called psychometric g in psychometric literature), underlying everything, at a third level (Carroll 1993; Deary 2000; Gustafsson and Undheim 1996; Hunt 2011; Jensen 1998; Johnson et al. 2008).

There is wide variation between studies concerning the number and identity of the narrow abilities, depending upon the particular focus of different studies. In terms of the present architecture, core processes and mental operations within each of the various domains reside at this level of narrow abilities. However, there is wide agreement as to the number and identity of systems at the second level. All studies speak about six to eight abilities. There is complete agreement about the domains of spatial, quantitative, verbal, and social thought. These are recognized in all traditions of research. The domains of categorical and causal thought are not directly mentioned in psychometric research, but they are both associated with inferential processes residing at higher levels, that is, either with fluid intelligence or g itself. Case et al. (2001) showed that all domains of thought noted above can be abstracted from performance on developmental tasks and tasks drawn from the WISC-R intelligence test.

General intelligence has been variably defined. For Spearman (1904) and Carroll (1993), g is mainly defined by inferential processes underlying reasoning and problem solving activated by the tasks. In fact, content-free tests of g, such as the Raven’s Progressive Matrices, are considered to tap these processes. Thus, these scholars believe that g is mainly

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Fig. 2 Idealized structural model for the architecture of mind proposed by the theory

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located in the inference panel and the domain-inference arrow in Fig. 1. Cattell (1963) differentiated between fluid (Gf) and crystallized intelligence (Gc) and suggested that fluid intelligence refers to general reasoning processes used to solve unfamiliar problems and that crystallized intelligence refers to skills and knowledge acquired as a result of the operation of fluid intelligence and have been crystallized for future use. Gustafsson (1984) showed that g and Gf are psychometrically identical.

With the years, Gf was gradually identified with working memory and executive control. Kyllonen and Christal (1990) showed, in a highly cited study, that working memory determines the condition of psychometric g almost completely. Several authors, following this line of research, showed that working memory and executive control largely determine the condition of Gf and psychometric g (Blair 2006; Colom et al. 2004; Conway et al. 2002; Kyllonen and Christal 1990; Miller and Vernon 1992). In fact, Engel de Abreu et al. (2010) showed that cognitive control mechanisms, rather than storage as such, are the source of the link between working memory and fluid intelligence.

In terms of the present theory, psychometric g may be seen from two different but interdependent perspectives. On the one hand, g is based on the two main mediatory constructs which serve the representation (representational capacity) and the integration (inference) of domain-specific representations. On the other hand, it depends on the quality of communication between all of the systems and functions involved at a given moment: how efficiently and accurately the SSS activated deliver their content for representation to the representational system, how precise and flexible is executive control until attaining a goal, how relevant are the models constructed and considered in the process, how timely and efficient was inference when making decisions, and how accurately and stably are new mental units metarepresented for future use (Demetriou and Kazi 2001, 2006; Demetriou et al. 2008). Therefore, g also resides in the arrows standing for the flow of interactions between constructs, in addition to the constructs themselves.

Interestingly, van der Maas et al. (2006) have shown that the correlations between abilities underlying g can be accounted for by the dynamic reciprocal relations between cognitive processes rather than by an underlying common cognitive or biological process or capacity. The present architecture lends support to this dynamical conception of g and shows how the various processes are interrelated. Also, it is consistent with Jensen’s (1998) conception of g. Jensen suggested that g is a biological rather than a psychological or behavioral construct, which reflects the ability of the brain itself to process information. In agreement with this conception of g, there is extensive research showing that speed of processing is a background factor that is related to psychometric g and that the strength of relation increases with the complexity of the tasks used to measure it (Demetriou et al. 2002, 2008, 2010a, b; Salthouse 2000; Sheppard and Vernon 2008) or with the effort required to learn a new task (Hunt 2011).

The present theory seems to resolve the long-standing debate over the nature of intelligence as either general or domain-specific (Carroll 1993; Jensen 1998; Gardner, 1983; Hirschfeld and Gelman 1994). The theory suggests that it is both. On the one hand, there are systems and processes which participate in every directed mental action, although their combination and effect may vary with task-related experience and development. On the other hand, there are domain-specific processes whose domain specificity varies according to the level of organization of mind concerned. Core processes in each of the SSS are strictly domain-specific because each is activated by a specific pattern of perceptual information which has the affordance to activate it. Mental operations within the SSS are by and large domain-specific. However, they are subject to development and training, and coordination between SSS. Knowledge and beliefs are object- or theme-oriented and

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therefore span over different SSS by definition (Demetriou et al. 1992, 1993; Efklides et al. 1992; Kargopoulos and Demetriou 1998). For example, a theory about motion in physics involves a causal component about the factors that can cause motion, a quantitative component that shows how causes and effects are associated with each other, and a visual– spatial model that represents these relations. Obviously, knowledge and beliefs in our theory correspond with Gc in psychometric theory. The various networks of concepts at this level are usually domain-specific in their meaning and in how they are represented, but they can be interconnected and translated to each other by the general processes specified by the theory.

An important aspect of domain specificity is representational specificity. Specifically, each SSS is symbolically biased to symbolic systems that are the most conducive to the representation of its own elements, properties, and relations (Demetriou 1988; Demetriou and Raftopoulos 1999). For example, quantitative thought is biased to mathematical notation, mental images are more appropriate than numbers or words to represent object characteristics and relations in space, and words are more appropriate than numbers or images to represent syntactical and propositional logical relations. Symbol systems can be translated into one another, but some information may be lost at the expense of other information when shifting from one system to the other.

Functional loops of the mind

From the point of view of education, which is the focus of this essay, it is important to stress that the architecture above is a dynamic system where all components are in constant interaction and interdependence. As indicated above, the power of this architecture with regard to problem solving and learning relies in the flow of effects from system to system rather than in the systems themselves. Several loops of processing may be discerned in this architecture. We will focus on two here, namely, the problem-solving and the self-awareness and control loops.

As shown in Fig. 1, the problem-solving loop is primarily activated by environmental stimuli and is therefore activated from within any of the domains. Once activated, it must first build a representation of the problem goal or the concept to be understood in the representational space (i.e., the SSS–STSS channel). An execution plan must then be planned which includes the necessary intervening steps or sub-goals to be made until attaining the final goal. These steps must be evaluated for their relevance to the goal, rejected or inhibited if not relevant, and ordered in an executable plan if relevant. Thus, planning for problem solving requires mental modeling of alternative or complementary paths to the final goal. Inference is needed at any moment in the planning process in order to judge the relevance or usefulness of sub-goals and the value of alternative sub-goal sequences. Duncan’s (2010) conception of g is relevant. According to Duncan, g is equivalent to the ability to build cognitive enclosures. That is, sequences of small mental action plans are embedded into each other such that each one delivers its product to the next one in the sequence until a final goal is attained. The longer the sequence or the more integrated detours it involves, the more powerful g is. This very process requires, one way or another, the construction of alternative mental models until a satisfactory representation or solution will be reached (i.e., the representational capacity–inference channel).

In the process of checking and selecting between representations and models, the second loop is needed. The self-awareness and control loop guides and protects the problem- solving loop through executive control (i.e., representational capacity–consciousness channel), it feeds it in with already available knowledge and experience by calling upon the mental maps available, and it provides evaluations that can guide decision making about

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the value of processing so that it may be carried on or interrupted any time. In short, this loop directs the application of reasoning and inference for the selection of the best problem- solving practices available under the circumstances (i.e., the problem-solving–domains channel). In this process, evaluations and comparisons between mental experiences and models may be reduced to other representations and principles that can be used in the future more efficiently and flexibly. This is the process of metarepresentation, which generates new representations and inferential possibilities to be called upon in the future.

Dynamic developmental relations

This architecture of mind implies that the various systems are dynamically and reciprocally related during development. The neo-Piagetians were the first to demonstrate empirically that increases in working memory capacity are systematically related to the development of thought (Pascual-Leone 1970; Case 1985, 1992; Demetriou et al. 1993, 2002, 2010a, b; Halford 1993; Halford et al. 1998; Mouyi 2008). This relation was ascribed to increasing processing efficiency as indicated by the speed of processing (Case 1985; Demetriou et al. 2002, 2008; Kail 1991, 1993) and control of attention (Demetriou et al. 2008; Posner and Rothbart 2006). Indeed, a large number of studies show that reaction times decrease with age in all domains and in all types of efficiency functions, reflecting the improvement in communication between the modules and levels of the mind (Case 1985; Case et al. 1996; Demetriou et al. 2002, 2008).

Mouyi (2008) (see also Demetriou et al. 2010a, b) demonstrated clearly these relations. She showed (see Fig. 3) that with increasing developmental level of inductive and deductive reasoning, reaction times to speed and control of processing tasks decreased and working memory capacity increased. In line with these findings, Barouillet and colleagues (Barrouillet et al. 2000, 2008; Markovits and Barrouillet 2002) showed that the development of deductive reasoning is a process of constructing mental models for real problems based on the content and knowledge available. The complexity of the models depends on working memory because more capacity allows for more models and more pointers from them to information in long-term memory. Awareness of this process and the ensuing executive control are important because they direct the final selection of models vis-à-vis the goal and their encoding into logical forms to be recalled later on.

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Fig. 3 Developmental levels of reasoning as a function of control of processing and working memory

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The architecture proposed here also predicts that acquisition of cognitive self-monitoring and self-regulation skills influences representational capacity and processing efficiency. In line with this prediction, Camos and Barrouillet (2011) showed that acquisition of rehearsal strategies between 7 and 9 years of age is related to an enhanced short-term storage capacity. Moreover, recent research suggests that increasing awareness of cognitive processes is related to a shift of reasoning from procedural (system 1) to algorithmic reasoning (system 2). Handley et al. (2004) found that the ability to decontextualize thinking from beliefs is associated with inhibitory control. Also, Klaczynski and Daniel (2005) showed that performance on modus ponens tasks is largely automatic and experientially based unless distractors are present, which must be processed and removed. However, consciously controlled analytic processing predominates on the uncertain forms (affirming the consequent and denying the antecedent) and on modus tollens, which require a suppositional attitude and systematic reflection on the meaning of the premises.

Also, there is clear empirical evidence about the interrelations of the processes in the problem-solving loop. English (1998) showed that deductive reasoning is important in the proper streaming of the sequence of mental models needed to represent the goal, assemble the steps needed to reach the goal, and choose actions. Albert and Steinberg (2011) showed that impulse control, which is part of executive inhibition, enables the thinker to design a full solution plan from the beginning, and working memory capacity is important for the representation of the plan, especially in problems requiring many steps to be solved. Interestingly, English (1998) showed that the relational complexity of the models needed is more important than their sheer number.

According to Halford et al. (1998), the relational complexity of concepts depends on the number of entities or the number of dimensions that are involved in the concept. For example, to understand any comparison between two entities (e.g., “larger than,” “better than”), one must be able to represent two entities and one relation between them. To understand a transitive relation, one must be able to represent at least three entities (e.g., objects A, B, and C) and two relations (e.g., A is taller than B; C is shorter than B). Numerical analogies, or algebraic relations such as 7 þ x ¼ 9 þ y, require four entities and two relations. Higher representational capacity allows models of higher relational complexity. In turn, this provides the possibility of more holistic representations to be conceived and longer or alternative solution sequences to be envisaged and evaluated against each other. As a result, problem solving can shift from isolated and thus frequently inefficient problem-solving strategies to more inclusive and thus more flexible strategies (Fireman 1996).

Our analysis about the role of consciousness in intellectual development is consistent with Zelazo’s (2004) model of the levels of consciousness. According to this model, the depth of reflection associated with each successive level of consciousness (LOC), first, adds depth “to subjective experience because more details can be integrated into the experience before the contents of consciousness are replaced by new environmental stimulation. Second, each added degree of reflection (higher LOC) causes information to be processed at a deeper, less superficial level, which increases the likelihood of retrieval. Third, higher LOCs allow for the formulation and use of more complex knowledge structures” (p. 16). In conclusion, it is clear that increasing processing efficiency enables the individual to keep in focus more representations, work out their relations more exhaustively, and metarepresent new representations that may be created for possible future use. Self-awareness and ensuing control of reasoning processes improves efficiency in handling available representational resources and help the developing mind raise mental models and processes into logical argument forms.

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In line with these findings, dynamic systems modeling suggests that changes in higher- level processes positively influence more fundamental processes (Case and Okamoto 1996; Demetriou et al. 2002). This is understandable from the point of view of modern dynamic systems theory. Higher-level structures, when attained, affect the operation of lower-level structures (Whitherington 2011). When attained, walking, as a complex coordination of the movements of the whole body, beneficially affects the movements of the feet, the hands, and the head. The mental number line, when consolidated, facilitates the specification of relations between different pairs of numbers (Dehaene 1997).

Mechanisms of cognitive development

Why does the mind improve over time? As noted above, the improvement of mind originates from the changes in any of its component processes. Specifically, novelty in the environment necessitates changes in the SSS. Novelty abounds in the environment. For one thing, the physical and social environment is rarely stable. By definition, changes in the environment beyond the control of the individual necessitate learning that would transform new information into knowledge available for future use. For another, education is the institution for cognitive change par excellence. As an institution of learning, school is a burden with the responsibility to tune individual cognitive development with past and current culture and civilization so that individuals acquire age-appropriate knowledge, understanding, and problem-solving strategies and skills.

According to the theory, all systems are interdependent. Dealing with new information or problems activates both the problem-solving and the self-awareness and control loops mentioned above. Thus, given the particularities of the new information or problems vis-à- vis the related old ones, such as differences in the volume and kind of information that must be handled, novelty activates a chain of changes affecting the executive processes needed for efficient use of representational capacity, the inferential and the mental modeling processes needed to associate the old with the new and reach a conclusion, and the metarepresentational processes needed to lift successful intuitions and discoveries into knowledge and skills usable in the future.

Jensen (1998) argued that g is learning. Provided that the two loops proposed here are complementary components of g, Jensen’s position may be read both ways: Learning is g as well. In other words, educating these loops can expand and refine both domain-specific mental operations and domain-general reasoning patterns that can bring to bear on the solution of new problems in the domains themselves.

How does the mind change? It is reminded that reflective consciousness is the mental tool for the activation and refreshing, comparison, variation, and mapping representations onto each other in order to deal with novelty and that metarepresentation is the mechanism that generates new representations out of these processes. This is a domain-general mechanism of cognitive development that feeds in a continuously expanding language of thought (LOT) (Fodor 1976). LOT includes a metalanguage of thought (i.e., general instructions about how to deal with new knowledge and new problems) and processes for the transfer of meaning from extant knowledge to new information (i.e., induction and analogy) and for the evaluation and selection of interpretations and decisions (i.e., deduction). Of course, this ever emergent process is always shaped under the constraints of the mental resources available and the motivation to think. The motivation to think requires schools that value and foster reflection and mental constructions. Below, we explicate this mechanism.

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From inference and problem solving to the language of thought

The core operators in the various domains are inferential systems that mediate between perception and particular organism-important patterns of information in the environment from very early in development. New mental operations and concepts develop through modification and differentiation of the core operators. Modification is possible when the patterns of information are close enough to the prototypical pattern to activate the operator but they also deviate from it so that a process of mapping is activated that will transfer meaning from the operator to the new information structure. The fundamental mechanism that permits mapping variations onto the core operators or onto each other is a three-step process: (1) abstraction, which extracts features from the input that can be fed into (2) a similarity search process and (3) induction that maps the abstracted feature onto the core prototype or another object and establishes a relation. Analogical reasoning is nothing more than an extra step in the inductive process that establishes a relation between two (or more) relations. According to Gentner (1989) and Gentner and Rattermann (1991), this extra step is a relational shift that enables children to shift processing from the similarity between objects based on an abstracted common feature to a similarity between relations (pairs of objects having the same relation). Similarity search and inductive decisions may be saturated by statistical regularities that may enable the infant to make predictions of the future state of events based on their present patterning (Téglás et al. 2011).

Some scholars (Braine 1990; Rosch 1975) do claim that there are pre-logical inferential schemas very early in development that are antecedent to deductive reasoning forms proper (such as joint iteration, which refers to repetitions of actions and is the basis for conjunction or permission rules or contingencies, e.g., “if you do this I will do that,” which function as the basis for implication). However, these become deductive schemes only when integrated across occasions and contexts and used as such. Therefore, a universal LOT is gradually constructed when similar patterns of thought across domains are compared and reduced to inference schemes that are explicitly represented in or tagged with natural language elements (e.g., “and … and” “or … or”, “if … then”) or other types of representations (e.g., mathematical notation) and are intentionally activated independently of context or domain.

A good sign of the state of this general LOT is logical necessity. When attained in development, it indicates that an inference scheme is lifted from a context-bound processing frame to an advanced rule-bound organizer of relations. Exceptions, when encountered, are considered as noise caused by errors in observation rather than as a fault of the inferential process. Under this condition, the general LOT becomes a powerful tool for checking the accuracy and precision of information and knowledge, and the consistency between alternative models or representations of it. Recent research shows that some grasp of logical necessity is present at the age of 7 years and is fully established by age 10 or 11, when children realize “that necessary truths are true everywhere, will never change, and cannot be imagined to be different [and] they refuse to draw a violation of a logical truth” (Miller et al. 2000).

Metarepresentation

The construction of new mental units is not tantamount to having them available for future use unless particular measures are taken for their preservation. New mental units are lost if they are not connected to symbols which will make them available for future use. Therefore, the endurance of new mental units depends, first, on metarepresentational awareness of the role and functioning of mental constructs. Second, it also depends on a

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process of symbolic individuation which pairs newly generated ideas with specific symbols with the aim of making them usable in the future (Demetriou and Raftopoulos 1999). These symbols, which may be idiosyncratic, such as mental images or personal scripts, or conventional, such as language or mathematical notation, fix and individuate a new representation so that it can be recalled, related to other representations, and mentally processed as needed.

Dual representation is the minimum requirement for using symbols to stand for objects or ideas. In addition, the thinker must be able to relate the referent with the symbol and mentally fix this relation so that the symbol always denotes the referent in the future, unless otherwise decided. Thus, the ability to metarepresent indicates an understanding of the asymmetric relation between the symbol and the referent (e.g., maps and scale models are much smaller than the depicted reality and may not include everything that exists in reality), and it is gradually differentiated according to several important parameters. The representational capacity available is crucial because it frames the complexity of reality– symbol relations that can be processed. The characteristics and demands of particular symbol systems, such as drawings, writing, and numbers, are also important, and they need to be learned as such if they are to be efficiently used as representations of reality (Yamagata 2007).

By the age of 4 years, children are adept in understanding the representational nature of different forms of symbols (Yamagata 2007). In the years following, children become increasingly flexible in adopting representations for their learning and understanding needs and for inventing their own representations. For example, research suggests that there are representational shifts during category learning such that, with learning, representations change from rules representing a single dimension to similarity-based exemplars integrating multiple dimensions (Johansen and Palmeri 2002).

Developing metarepresentational competence is increasingly intertwined with inferential capabilities and increasing mastery of dominant symbol systems, thereby enhancing the scope and power of the universal LOT. Natural language has a privileged relation with this emergent universal LOT because it is the main symbol system that can be used to express and manipulate LOT’s components. One may imagine many examples of conversations in which the linguistic and the actual structure of events are in direct correspondence: “If you drop it, it will break into pieces, you see!” Natural language and the emergent LOT are gradually intertwined and used to guide and facilitate inference and processing within each of the domains. In line with this assumption, the permission schema theory (Cheng and Holyoak 1985) posits that these inductions occur in the context of permission rules which specify the conditions under which a given action can or must occur (Harris and Nunez 1996).

Also, Raffray and Pickering (2010) showed that thinkers first construct meaning-based representations based on thematic context and then abstract logical form when processing verbal information that is to be interpreted logically. For example, when processing sentences including quantifiers, such as “every” and “a,” which require one to specify the number of actors, objects, and activities, thinkers abstract the universal and the existential quantifier from meaning-rich representations exemplifying the quantitative relations in the components of the sentence. Also, Fisher suggests that object naming functions in infancy as a pointer to similarity between objects, thereby guiding the induction of similarity relations between objects. Specifically, a common name for objects that do not appear completely identical directs 3- to 5-year-old infants to look for, represent, and tag similarities (Fisher 2010). Therefore, the symbolization process partakes in inferential processes by guiding the transfer of meaning across representations.

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A study by Gauffroy and Barrouillet (2011) lends support to the assumption that deductive reasoning development evolves from the handling of mental models implement- ing possibilities to the grasp of the logical relations of truth–falsity implied by the models. This study showed that basic logical relations are first grasped through the construction of mental models that translate a proposition (e.g., “if the box is red it contains cherries”) into all possible compatible and incompatible. Specifically, conjunction, which requires only one model, is grasped at about 8 years of age (e.g., red box with cherries); the biconditional, which requires two models, is grasped at about 11 years (e.g., red box with cherries, green box with apples); the conditional, which requires at least three models, is grasped at about 15 years (e.g., red box with cherries, green box with apples, red box with apples). When these very same possibilities are expressed in terms of statements that are true or false, given the initial proposition, each of these relations is grasped 2–3 years later. Obviously, with experience and ensuing reflection, the set of mental models that are compatible with the various types of relations are condensed into a universal rule that can be used to fully generate or evaluate them, if needed. We will show below that this conception of development is important for education in reasoning and critical thinking.

Developmental trends and individual differences

It is stressed that the theory presented here is not a stage theory of development. In fact, the complexity of the dynamics between systems and processes in development are such that there are no hard or easily noticeable boundaries between developmental phases. There always are precursors before a particular milestone is reached, and its consolidation takes time. In terms of the dynamic systems theory (van Geert 1994), developmental milestones operate as pivotal points toward which precursors tend to stabilize and subsequent changes tend to be organized until the attainment of the next milestone.

Moreover, the general trends in development described above do not imply that the likelihood of attaining each of the developmental milestones is the same for all individuals or that development proceeds at the same rate across domains. On the one hand, there is strong evidence that the frequency of each next milestone decreases systematically in the general population. Specifically, the first three milestones are closely related with age and are attainable by most children. However, conditional and principled representations are not attained by the majority of persons and are very weekly related to age. Shayer and colleagues (Shayer et al. 1976; Shayer and Wylam 1978) showed that the higher levels of Piagetian formal operations are not very common in the general population, while Commons et al. (1982) showed that postformal levels of functioning are very sparse even among college students (see also Moshman 2011). On the other hand, however, if we translate our position that a LOT is gradually constructed that expands with development in psychometric terms, we would predict that g would increase with age. Evidence shows that this is indeed the case. Pind et al. (2003) showed that scores on the Raven’s Standard Progressive Matrices increase systematically from the first to the tenth school grade, suggesting that general cognitive competence increases with age.

However, it is noted that operations and concepts within each of the domains are only built as a result of experience and interaction with the domain concerned. Therefore, there may be extensive variations in the rate of development across domains and individuals, depending upon the person’s engagement with the various domains (Demetriou et al. 1993, 2002, 2010a, b). In fact, Ackerman (1996, 2000) suggests that, with age, general cultural knowledge and more specialized knowledge related to an individual’s social group and professional activity acquire prominence over general or fluid intelligence. In terms of the

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present theory, this would imply that the possibilities afforded to an individual by a particular profile of processing and inferential possibilities must be invested in readily available domain-specific, socially, and culturally relevant skills and knowledge. With age, this investment must be increasingly expressed through both a particular domain of professional activity and more general domains associated with culturally important roles, such as the role of the parent, the friend, etc.

Towards a Theory of Instruction

Developmental constraints and educational ideals

In this section, we outline the implications for education of the theory summarized here. The architecture of developing mind outlined here suggests that domain-specific systems always work together with general mechanisms and processes. All SSS and all general mechanisms interact variably with the various knowledge domains to be mastered at school. Therefore, the ideal of education would be to positively influence all of these processes depending upon (1) developmental time (i.e., the age and developmental condition of the students), (2) educational time (i.e., the grade and prior educational experience and knowledge already attained as specified in the curriculum), (3) the particularities of the subject matter or knowledge domain concerned, and (4) the structural and procedural aspects of education that are crucial for learning, such as the education of the teachers, teaching methods, technological support of learning, etc. The following issues will be discussed below:

1. Schooling–developing mind relations. It is important to specify how mental processes and capabilities are related to school performance and how schooling impacts mental processes and capabilities.

2. Tuning educational timing with developmental timing. Education should be guided by an overall roadmap that takes into account how the developing person relates to the world at different phases of development. Therefore, the developmental milestones outlined here must inform educational priorities for different phases of life. In addition, learning demands at successive school levels and grades must respect the representational and processing constrains and possibilities of the students’ developmental level.

3. Mental modeling. Students should practice the ability to create and use mental models for the sake of understanding and problem solving. Mental model building and management must be an integral component of education for critical and creative thinking.

4. Domain-specific learning and conceptual change. It is clear that instruction must sharpen, refine, and muster subject-specific processing and problem-solving processes and skills. Instruction must generate controlled conceptual change so that students acquire new grade-appropriate knowledge in the various school subjects and rectify the various misconceptions they may have. Ideally, by the time they leave school, students must have a sound understanding of the fundamentals of knowledge about the world as represented by the sciences and disciplines of our time.

5. Inference, metarepresentation, and learning to learn. Students must be directed to generate, strengthen, and enhance general reasoning patterns and problem-solving skills out of handling and managing mental resources. Provided that reasoning emerges out of metarepresentational processes, metarepresentation must be a target of education in itself.

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6. Critical thinking and creativity. Students must be critical so that they can evaluate information and knowledge for its value relative to goals, either personal or social. Evaluation must be related to criteria of accuracy, sufficiency, validity, and usefulness. In a sense, building these criteria is the ultimate goal of education.

Relations between developing mind, school performance, and learning

The relation between cognition and education is bidirectional: Cognition shapes and affects learning in schools, and schooling shapes and affects cognition. In this section, we will summarize studies highlighting how this bidirectional relation evolves from preschool to early adulthood.

Intelligence–schooling relations

Many studies substantiate the relation between the various dimensions of the mind specified here and learning at school. Specifically, the relation between performance on intelligence tests and examinations reflecting school achievement are very high (approx. 0.8 for the SAT and the GCE). The relation between intelligence scores and school marks is lower but considerable (approx. 0.5).

Deary et al. (2007) showed that general intelligence at the age of 11 years strongly predicts school achievement at the age of 16 years all over 25 school subjects examined (the relation between the two latent factors was 0.81). The relation varied extensively across the various subjects. Specifically, the variance accounted for ranged from 58.6% in Mathematics and 48% in English to 18.1% in Art and Design. For those at the mean level of g at age 11, 58% obtained five GCE at the age of 16 years. A standard deviation increase or decrease in g altered the values to 91% and 16%, respectively. Rohde and Thomson (2007) showed that fluid intelligence as reflected in performance on the Raven Matrices is a very strong predictor of school achievement, as reflected in the SAT. However, they also found that different aspects of school achievement are differentially related to the various aspects of g. That is, general cognitive ability was a stronger predictor of SAT verbal than working memory, processing speed, and spatial ability. In contrast, spatial ability and processing speed predicted SAT math scores in addition to the contribution of general cognitive ability.

In a thorough study, Rindermann and Neubauer (2004) showed that processing speed influences both general intelligence and creativity and that these, in turn, influence school performance. Krumm et al. (2008) showed that reasoning is a good predictor of school achievement in science (mathematics, physics, biology, chemistry) and language. The executive and coordination processes of working memory additionally contribute to performance in science; verbal storage additionally contributes to achievement in language. Therefore, it seems that different subjects in school require a different combination of domain-specific and domain-general processes depending upon their requirements. It needs also to be noted that general intelligence is more important for individuals of low rather than of high intelligence (Detterman 2000).

A series of studies conducted in our laboratory explored these relations systematically from primary to high school in order to specify whether they vary with development (Demetriou 1998, 2000, 2010a, b). One of these studies (Demetriou et al. 2010a, b), which included 8- to 12-year-old students, found that a very large part of the variance of each of three school subjects is highly accounted for by speed of processing, working memory, and

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problem solving in three SSS (i.e., in total, 79% in Science, 72% in Mathematics, and 88% in Greek). Of the three dimensions, the strongest predictor was always the problem-solving factor underlying the three domains (i.e., 55% in Science, 67% in Mathematics, and 74% in Greek). The effect of processing speed, although considerably lower (i.e., 21% in Science, 4% in Mathematics, and 10% in Greek), was always significant. The effect of working memory was very weak (lower than 4% in all cases).

A second study (Panaoura, Gagatsis, and Demetriou 2009) involved children and adolescents from 10 to 18 years of age. This study found that the relation between processing efficiency (speed and control of processing) and school performance was much stronger than in the previous study (i.e., it accounted for 50% of the variance in Science, 25% in Mathematics, and 16% in Greek). This may reflect the fact that the importance of general cognitive efficiency as such for learning becomes stronger with age until early adulthood (Grigorenko 2002). Problem-solving processes underlying performance on the five thought domains (which have a strong Gf component) accounted for only 3% of performance in Science, 6% in Mathematics, and 34% in Greek. Crystallized intelligence accounted for 25% of the variance in Science, 25% in Mathematics, and 31% in Greek. Interestingly, processing efficiency and reasoning were important factors of school performance in all subjects throughout adolescence. The relation of school performance with crystallized intelligence became increasingly stronger. This is consistent with the developmental tendency noted earlier that from adolescence onwards, when basic knowledge extraction mechanisms are consolidated, knowledge acquisition and use become more important.

A third study (Demetriou and Panaoura 2006) aimed to assist learning in mathematics and specify the involvement of the various dimensions of mind, as they were presented above. This study, which involved 9- and 11-year-old students, found that children who were high in processing efficiency and working memory were equally able to profit from plain exposition to the teaching material or systematic instruction addressed to mathematical problem solving. However, those who were low in these two fundamental parameters of cognitive functioning were able to profit from systematic instruction, but not from plain exposition to the teaching materials. The message of this study for learning is clear: A well-structured learning environment enables the students who are weak in their processing and representational capabilities to learn despite their weaknesses. However, the students who are strong in processing and representational capacity can themselves compensate for the shortcomings in their learning environment because they learn fast and efficiently, thereby discovering and constructing by themselves the relations and concepts that float, so to speak, in the information provided. Also, Panaoura et al. (2009) showed that the more reflective and self-aware a person is, the more this person profits from instruction.

Hundreds of studies examined the effect of schooling on intelligence. The Abecedarian Study of Campbell and Burchinal (2008) was addressed to underprivileged children who were followed from early infancy to early adulthood. Treated participants consistently and significantly outperformed controls throughout the age phase studied, although their difference decreased systematically from about 10 IQ points in the age phase 2–5 years to about 4.5 IQ points in the period from 6.5 to 21 years. Interestingly, the difference in IQ after the age of 12 years was due to the differences in verbal ability in the early preschool years. Overall, these studies suggest that there is an increase of something between 1 and 4 IQ points for each extra year of schooling (Ceci 1991; Winship and Korenman 1997). Gustafsson (2008) found that this effect came from changes in Gf rather than in g as such. Interestingly, this effect came mainly from academic rather than from vocational programs

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of study. Moreover, he found that programs oriented to science and technology exerted a positive effect on visuospatial ability and crystallized intelligence.

Cognitive development–schooling relations

Kyriakides and Luyten (2009) studied the relation between schooling and cognitive development from 12 to 18 years of age. They used a comprehensive test of cognitive development (Demetriou and Kyriakides 2006) that examines the student’s level of reasoning in all of the domains of thought described here but social thought. To specify the effect of schooling, they compared students of the same age who differed by one school grade due to the fact that there is a conventional cutoff point in birth date which specifies when students enter primary school. They found that the effect of one extra year of schooling was significant and considerable (effect=0.33, Cohen’s d=0.61) throughout the six secondary school grades. This effect suggests that more school experience accelerates progress along the hierarchy of cognitive developmental levels. The effect of schooling on mathematics (effect=0.87, Cohen’s d=0.68) and language (effect=0.87, Cohen’s d=0.72), expectedly, was larger. This is an interesting finding, showing that schooling, in addition to augmenting school-specific knowledge, also boosts the underlying mechanisms of cognitive development as such. The findings of Artman et al. (2006) are in line with this interpretation. They found that schooling enabled 12- to14-year-old adolescents to accept the verbal statements in an argument as premises calling for the specification of their logical relations rather than of their content.

The research of Shayer and Adey (2002) on the acceleration of cognitive development points to the same conclusion. These scholars showed that scaffolding cognitive development through controlled cognitive conflict, social support, and reflection produces immediate gains in the level of cognitive development, but also to subsequent gains in national public examinations taken 3 years later, in Science, Mathematics, and English language (Shayer and Adey 2002; Shayer and Adhami 2003).

Some aspects of culture which are tightly intertwined with education may be strongly associated with intellectual functioning. Research shows that this may indeed be the case with writing, which is major component of education. Logographic systems of writing, such as Chinese, are very different from alphabetic systems in their cognitive demands. That is, logographic systems are much more demanding than alphabetic systems in visuospatial processing and construction of associations between meaning and representations. Demetriou et al. (2005) maintained that learning the Chinese logographic system has a lasting effect on visuospatial processing that extends from basic information processing mechanisms related to speed and control of processing to visuospatial spatial working memory and spatial reasoning. Recent studies substantiate this claim. Cole and Pickering (2010) showed that learning Chinese makes children more flexible in using both visuospatial and phonological encoding strategies for storing and retrieving information from working memory as contrasted to learners of alphabetic orthography who use only phonological strategies. McBride-Chang et al. (2011) showed longitudinally that readers of logographic systems outperform readers of alphabetic systems on visual tasks and that learning to read Chinese has a general beneficial effect on visual–spatial reasoning. These findings may explain, at least in part, the fact that eastern nations (i.e., Chinese, Koreans, and Japanese), who use logographic systems of writing (average IQ in the range 105–108), outperform western nations (average IQ in the range 90–100) in measures of intelligence (Lynn and Vanhanen 2006).

In conclusion, there are well-documented reciprocal relations between the developing mind and schooling that are related to all aspects of the cognitive architecture depicted here.

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The discussion below builds on these relations in order to suggest how education can be more systematic and efficient in improving the students’ minds and learning.

Self-awareness, personality, motivation, and schooling

The relation of self-awareness and self-representation with school performance is much more complicated and indirect rather than direct. Specifically, all of the studies summarized above showed that cognitive self-concept as such is very weakly, if at all, related to school performance, especially in primary school. However, accuracy in self-evaluation is related to school performance in adolescence. Specifically, we found (Demetriou and Kazi 2001) that adolescents who were not accurate in their evaluations of their own performance on cognitive tasks tended to obtain lower school grades than their classmates who attain the same cognitive performance and were accurate in their self-evaluations. Interestingly, high cognitive ability and self-evaluation accuracy go with a strong sense of self-control. Moreover, these individuals tend to engage in activities which require effort and originality. Overall, however, the additive contribution of personality factors to school performance is very low. That is, each of three personality factors, i.e., conscientiousness, openness to experience, and persistence to stay on goal, accounted for an extra 4% of the variance of school grades in the three school subjects above. It seems, therefore, that motivational and personality factors moderate the investment of cognitive factors into learning activities rather determine learning as such (Demetriou and Kazi 2001).

Tuning developmental and educational timing

Piaget was the first to note that children in different developmental stages live, in a way, in different worlds and that education must take this into account if it is to be successful. Therefore, learning tasks, to be successful, must be tailored to the child’s phase of development. This section will discuss implications of the theory presented here for the developmental timing (Anderson 1985) of educational goals, concepts, and problem-solving skills across and within school grades or other educationally meaningful time units.

Capitalizing on developmental milestones for learning (Table 4)

The sequence of developmental milestones is an important guide for the formulation of major educational goals. So far, state education does not include programs for children younger than about 3–4 years of age. The time is ripe for a more systematic approach to education in infancy. The rich knowledge that we now have about the mental capabilities of infants provides a sound basis for the design of “educational environments” that would enable the infants to consolidate and expand these capabilities. Moreover, the increasing demands of primary education necessitate the required preparations in infancy.

Infancy and core operators Education in infancy should be directed to practicing the core operators in the SSS in order to enhance infants’ (1) understanding of the nature, behavior, and relations of objects; (2) their own relations with objects and other persons and the influence of their own activity on objects and persons; and (3) the basics of number. Some examples are given below for each SSS.

Infancy is marked by limited representational capacity and executive control. Moreover, at this phase, the use of language as a medium of representation and instruction is limited.

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Therefore, education focusing on the core operations present before the age of 2 years must capitalize on the active discovery and exploration activities that dominate at this phase and control for the easily shifting attention and lack of language (Posner and Rothbart 2006).

Table 4 Developmental milestones and educational priorities for the education of the developing mind

Age and milestones

Educational priorities Learning to learn Epistemological awareness

Infancy: 0–2 Educating directed discovery and the SSS core processes. Infants must construct (1) a basic understanding of the nature, behavior, and relations of objects, (2) their own relations with objects and persons, the influence of their own activity on objects and persons, and (3) the basics of number.

Educate infants to follow instructions. Let them do complementary actions vis-à-vis a task of interest. Let them have “controlled failure” experiences.

Differentiate one’s own knowledge from others’ knowledge (e.g., dad can do things I cannot do and so I can ask for his help).

Elaboration of core operations

Toddlerhood: 3–4

Educating dual representation. Explore alternative appearances of objects, perspectives of persons, and representations of objects and persons and tag them with symbols.

Explore their own actions and talk about them. Talk about each others’ representations and doings. Differentiate between what they can and what they cannot do. Differentiate between knowing what to do and doing. Rehearse.

Overcome absolutism.

Dual representation

Primary school: 7–8

Educating logical exploration and coherence. Mapping object characteristics onto related mental operations, representations, and concepts. Explore relations between operations and representations and grasp how they constrain each other in meaning and conclusions.

Differentiate between mental functions (i.e., attention, memory, knowing, inference, etc.) and relate them with appropriate activities. Formulate plans of actions and tell them to yourself.

Understand the multiplicity of knowledge and that the nature and “quality” of knowledge depends upon the methods or processes generating it.

Logical necessity

Adolescence: 13–14

Educating suppositional thought. Educate the “if … then” stance. Envisage alternative worlds, test, validate, choose, and argue. Differentiate between logic and inference.

Differentiate between mental operations in different domains. Associate them with relevant representational, inferential, and logical systems.

Grasp the complementarity of methods and processes in knowledge production and revision.

Suppositional thought

Young adulthood: 20–22

Educating theory building. Educate on realism, relativity, and theory building, merging, and modeling.

Associate mind operations and products with culture and history. Differentiate knowledge construction and control methods according to historical era, discipline, or social context.

Understand the differences between traditions, schools of knowledge, epistemological paradigms.

Theoretical thought

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This can be attained by a careful organization of what is available to the infant to play with and discover the relations of interest. These must be limited in number so that the infant is not distracted by them.

For example, classification skills and induction of relations must be based on perceptual similarity that generates concepts in the categorical system. There may be special markers that can direct attention to particular features of the objects, such as shape or color, and facilitate the infant to ignore other features, such as texture. Moreover, there may be special effects that provide feedback about acceptable and not acceptable sorting actions, such as special sounds for successful and unsuccessful actions. These conditions may direct the infant to explore the form, size, shape, and relations between objects which may generate mental representations of the objects and their categorical and visuospatial relations. In the quantitative SSS, objects can be arranged variably within the subitization limit so that they can be counted, numbered, and tagged visually to generate representations about number. In the causal SSS, particular objects or components of objects may be associated with special effects, such as sounds or colors. The actions of the infant on these objects may enable him to build causal connections between his behavior, objects, and effects. Mapping variations of actions with corresponding variations of effects is conducive to the construction of a mental space of causality. Finally, in the social domain, pretend play may be used for recognizing emotional expressions, deciphering the emotional states of others in a social system, and understanding their effects on interpersonal interactions. It is interesting to note that the toy and the cartoon industry produces toys and cartoons that implement knowledge and ideas about the mental needs and capabilities of young infants. These products, if properly presented, may prove very conducive to the goals of infant education noted here.

At the edge between infancy and toddlerhood, with the emergence of language, education must enable the infant to connect core operators with representations and advance them into the respective mental operations of the specialized domains concerned. That is, the infant must be facilitated to recognize how different words, symbols, and the grammatical and syntactical structures of language are connected to realities related to each of the core operators mentioned above.

Education episodes in infancy must recycle with gradually shifting content and complexity to ensure that mastering of the target relations becomes consolidated and increasingly free of content and contextual restrictions. Education episodes in infancy must have a strong social component where empathy motivates the infant to engage in learning activity and imitation provides the frame for fast learning (Meltzoff et al. 2009). Moreover, individual differences in temperament and activity level must be taken into account for the formation of programs adjusted to different children (Keogh 2003). For example, some infants can focus and persist on an activity much more than other infants. Therefore, cycles of activity must be adapted to the focus span of the infant (Posner and Rothbart 2006).

Preschool and dual representations Education at the preschool years must provide conditions for the knowing and handling of dual representation. For instance, tagging photographs to the objects they represent and then varying the perceptual similarity between an object and its photograph until transforming the photograph into an abstract representation of the object is a method for inducing toddlers in the nature of representation in the categorical domain (DeLoache 1991). Tagging numbers onto sets of objects and tagging written words to expressions may have the same function in the quantitative domain and in the domain of language, respectively. Moreover, associating alternative

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representations with the same reality (e.g., number digits and number words) may be helpful in enabling the child to dissociate representation from thought as such.

Children must be systematically instructed to explore how the appearance of objects may change without affecting the identity of objects (e.g., changing the dressing of a doll, playing with transformers and other toys where variation of appearances is an integral part of their construction). They must be assisted (e.g., via role playing) to alternate between one’s own point of view and another’s point of view by systematically exchanging and comparing positions. Moreover, in these contexts, they must be induced to realize that different perspectives or different appearances may induce in different persons different representations and different beliefs about the realities concerned. Thus, tagging photo- graphs of the different perspectives from which an object may be viewed and associating it with different persons, according to the perspective from which they view the object, is a good practice into perspective taking and theory of mind. Therefore, in this phase of life, education must enable preschool children to join their own overt actions with alternative representations that may be used to represent them.

Furthermore, toddlers must learn to observe their actions relative to a task, observe others doing the same task, and reflect (or talk) on both how they can themselves modify actions in order to become better tuned to a goal and on others’ performance. Talking with others about an object or action helps in building a representational space where the same actions or objects are represented by different representations or different actions or objects may be represented by the same representation. In conclusion, bridging of self-modification experiences over different occasions must be encouraged as it strengthens self-regulation and executive control. Bridging actions and representations across persons helps differentiate how reality can be represented, how representations may be used to guide actions, and that alternative representations lead to alternative action plans.

Primary school, representational cohesion, and logical necessity Dual representation gradually evolves, in the primary school years, into coherent thought that can handle multiple representations. Therefore, education in the primary school years must focus on revealing the connections between concepts and operations and facilitate children to see their own overt or mental actions vis-à-vis the concepts and operations. For example, children must realize that sorting objects into different groups or stacks according to similarity results into category building. As a result, perceptual characteristics of objects and actions on them are reduced to mental representations and words standing for them. In turn, category building facilitates mental labor and learning because, for example, object characteristics may be logically derived from category properties and category names can generate the names of all objects included in each category. This will enable children to realize that series of representations are logically linked with each other so that particular series necessarily result into particular conclusions. This will help them understand that logic underlies inference and is governed by restrictions that need to be respected, if the outcomes of inference are to be valid (Barrouillet 1997; Daniel and Klaczynski 2006; Klauer et al. 2000; Overton et al. 1985).

Adolescence, suppositional, and theoretical thought The next milestone is the suppositional and theoretical stance of adolescence. Therefore, education in early adolescence should focus on developing the “if … then” stance approach to problem solving and build epistemological awareness about the characteristics, possibilities, and limitations of different knowledge domains vis-à-vis their methods, functions, and priorities. That is, adolescents should be able, on the one hand, to handle logical inference efficiently. On the

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other hand, they must realize that very often, logical conclusions may vary depending on the premises involved and that premises may be derived from different theories or belief systems. Eventually, however, an acceptable truth may be reached through appropriate controls, even temporarily.

A useful framework for the strengthening of this stance is the systematic exploration of important phenomena from the point of view of different disciplines or different theories within a discipline. Motion is a good example. There are theories about motion in practically every science. In physics, it is described in reference to speed and space and is explained in reference to causal factors, such as energy, force, and work. In chemistry, it is described in reference to the structural and molecular characteristics of objects. In biology, it is described in reference to its function (e.g., survival), the structural enabling mechanisms (e.g., feed in walking animals, wings in flying animals), and the biological enabling mechanisms (e.g., eating, digestion, photosynthesis, metabolism). In psychology, it is described as an aspect of development (e.g., changes in the ability for movement in the first 3 years of life), or skill acquisition (e.g., learning to drive a car), as a means for social interaction (e.g., approach to another person), and as the result of underlying psychological causes (e.g., the expression of an intention or the implementation of a mental plan). Adolescents may be acquainted with different models for motion in each of the disciplines mentioned above, explore their similarities and differences in concern to the methods used to construct them, the data invoked to support them, the language or symbol systems used to represent them, and their functional role in each discipline as a system of knowledge. Moreover, they may run experiments especially designed to demonstrate specific models in different disciplines. For example, students may study the similarities and differences between Aristotle’s, Newton’s, and Einstein’s models of motion in physics. Moreover, they may be acquainted with the use of concepts from one discipline to explain phenomena in another discipline (e.g., chemical reactions underlying the transformation of energy into work to explain metabolism enabling movement in animals) in order to grasp the general underlying mechanisms that cut across disciplines and use common models to represent them, such as the notion of function connecting variables of interest.

Adulthood, multiplicity of knowledge, rationality, and wisdom The goals of education for adolescence as stated above must culminate in the college level. The aim would be to refine conditional and theoretical thought and embed it into living science. Although a possibility of this phase of life, this stance is very rare even among college students despite the fact that universities take it for granted. Thus, in college, students must be given ad hoc education directed to the understanding of (1) science as a process of discovery and incessant change of knowledge and methods (2) and as an institution in interaction with other social and cultural institutions.

Courses on the nature and history of science would be conducive to the attainment of this aim. This knowledge would facilitate and consolidate the differentiation between knowledge and thought which is attained at this phase of life. Moreover, it would contribute to the understanding of the double nature of scientific knowledge. That, on the one hand, it is relative vis-à-vis other relevant theories of the past, present, and future, whereas, on the other hand, it is of certain heuristic value vis-à-vis relevant understanding and problem-solving needs. This would contribute to the refinement of critical thinking skills that are necessary to deal with complex problems more generally than the particular discipline of one’s study.

Not all youth go to university. Programs of lifelong learning may compensate for this lack. These programs may focus on issues related to life choices and their possible impact

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on one’s own and other persons’ lives. Taking the different perspectives and ensuing choices of different persons involved in life episodes would enhance one’s ability to alternate between choices and integrate accordingly. Rationality and wisdom may be facilitated.

Tuning educational demands with cognitive possibilities

According to the neo-Piagetians (Case 1992; Halford et al. 1998; Pascal-Leone 1988), to be grasped, concepts have a specific representational demand. Therefore, the concepts and activities to be taught must reside within the representational and processing possibilities of each child in the classroom. According to Halford et al. (1998), the mental demand of concepts is equal to the relational complexity of the mental units that they involve. Also, any sequence of concepts must be tuned to the needs of the developmental milestones of the phase concerned. This would, on the one hand, facilitate learning as such and, on the other hand, would consolidate the mental possibilities afforded by the developmental milestone of each developmental phase.

Moreover, concepts are usually defined on the basis of other concepts. For example, according to Dawson (2006), the concept of energy (as the ability to do work) requires the integration of three abstract concepts (e.g., kinetic energy, energy transfer, and gravity); these, in turn, are defined by simpler concepts (motion, pulling, pushing, etc.). Scholars (Case 1985; Demetriou et al. 2010a, b; Fischer 1980; Halford 1993; Pascal-Leone 1988) suggest that higher-level concepts can be grasped when the integration of the lower-level component concepts is representationally possible. Therefore, to properly teach the concept of energy, the educator should first establish the grasp of the lower-level component concepts at the right age.

Therefore, in preschool, teaching may focus on the activities of the infant, such as pushing or pulling and their results on objects. In primary school, students may reflect on the relations between the observations that give content to the component concepts, such as kinetic energy and energy transfer, with the aim of grasping the informational and representational cohesion that they entail. Subsequently, in early adolescence, students may reflect on the underlying principles that integrate kinetic energy and energy transfer and how they can be expressed into a single construct. At the end, the integrated concept of energy may be reflected from alternative points of view, such as from the point of view of physics, biology, ecosystems, etc. For example, teaching may elaborate on energy in diverse substances, such as oil and food, and the physical and biological mechanisms that can process it, such as engines and digestion and metabolic systems of animals. Obviously, the complexity of concepts and examples must remain within the representational capacity of each age level.

Also, it is to be noted that no individual functions continuously at his or her upper limit of capacity, especially at the initial phases of reaching this capacity or of implementing this capacity in the mastering of a concept or skill. Moreover, not all individuals in a classroom operate at the modal upper limit that is characteristic of the age of interest. In fact, research suggests that there may be systematic differences between the learning possibilities of children within the same classroom because of age differences at school entrance. These differences produce the so-called relative age effect in school performance. Cobley et al. (2009) found that the younger students in a classroom obtain significantly lower grades than their older classmates throughout the curriculum (i.e., science, maths, and physical education) across secondary school grades and gender. In terms of the theory presented here, this effect reflects possible initial differences in children’s cognitive ability (also

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possible differences in skills for handling the learning environment) which cause a disadvantage on their part to understand and follow the pace of their older classmates.

Therefore, a general guiding principle for curriculum designers and teachers is as follows: The pacing of teaching (Anderson 1985) of any concept or skill must always start with examples having a cognitive demand that is lower than the optimum capacity of each student in the classroom. Even high-capacity children may operate at a level lower than their optimum when first facing a new concept or skill. Therefore, provided that there is variation across individuals for a given domain and within individuals across domains, the mastering of a concept or skill must be graded along a continuum of examples that range from the lowest necessary to the optimum for each individual student in each domain. Our study summarized above about the relations between learning mathematics and processing efficiency has clearly suggested that when processing efficiency and working memory are weak, guidance is necessary to compensate for the students’ weakness to construct the necessary relations themselves. Concerning the relative age effect noted above, special provisions are needed that would enable the teachers to spot younger children in the classroom with possible understanding difficulties and provide them with special support to compensate for their difficulties. A good approach here is to start teaching a given target concept at level L by introducing the lower-level L-1, L-2, etc., component concepts of the target concept. Reviewing a large number of studies, cognitive load researchers have reached the same conclusion (Sweller 1994; Kirschner et al. 2006).

Therefore, each student must be given the opportunity to construct the concept or skill of interest gradually and steadily according to his or her own zone of proximal development and according to his or her own scaffolding needs. This kind of classroom management requires a flexible curriculum that allows individualization of learning in the same classroom so that treatment develops according to the aptitude of different students (Snow 1989). Below, we will focus on the education of the two main loops of understanding.

Educating the problem-solving loop

Educating representational and processing efficiency

An increasing number of studies tried to improve intelligence by training addressed directly to the various aspects of working memory, attention, and executive control (Buschkuehl and Jaeggi 2010; Jaeggi et al. 2008; Klingberg et al. 2002; Posner and Rothbart 2006; Thorell et al. 2009). This approach was based on the evidence showing that fluid intelligence is largely determined by these processes (Blair 2006; Kyllonen and Christal 1990). In all of these studies, training aimed to improve the ability to (1) monitor ongoing performance, (2) keep track of the flow of information and update it for as long as needed, (3) manage more than one task simultaneously (e.g., by alternating between blocks or types of information, if needed), (4) bind items according to type and time of presentation, and (5) inhibit irrelevant items. Clearly, these studies aimed to improve the quality of representation, management, and integration of information in working memory. Therefore, they directly addressed executive and episodic integration processes. In line with these findings, several studies found that these interventions resulted in improved performance on tests of fluid intelligence, such as the Raven Matrices. There is also evidence that training working memory has a long-lasting positive effect on the general intellectual and school performance of students diagnosed with ADHD (Westerberg 2004).

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Educating mental modeling and conceptual change

There is a vast literature on conceptual change and how it relates to education (e.g., Carey 1985; Vosniadou 2008). Education is a constant force of conceptual change because it imparts new scientific knowledge about the world that is often in conflict with knowledge coming from personal experience or important others. Therefore, teaching must rectify or displace the personal or lay knowledge available. This is not always easy because this knowledge is functional and supported by the phenomenal aspects of things. The heliocentric model of the world and the model presuming that the earth is flat are classical examples of this knowledge. These models persisted in human history for ages, and they still persist as spontaneous constructions of individual development (Vosniadou and Brewer 1992). Thus, teaching must lead the students to conceive of the new concepts and demonstrate their explanatory and problem-solving advantages relative to the old concepts.

Moreover, learning in schools is additive and takes place over many years. Thus, the very organization of teaching necessitates conceptual change because the concepts constructed at a given grade must be extended or connected with other related concepts that must be constructed at following grades. Also, knowledge changes as a result of new discoveries in science. Therefore, even knowledge presented by the school as proper at one time may be questioned by the school itself at some later time. Finally, knowledge about the same phenomena or realities may differ across different fields. Therefore, the school must develop a mindset for continuous conceptual change in students. This requires tools to represent and learn the new concepts presented in the classroom. Mental modeling is one of the main tools. Here, we will focus on the use of mental models in conceptual change. We will first elaborate on the construction of mental models for the use of the basic operations associated with the various operational domains. Then we will focus on the use of mental models for different knowledge domains and their role in conceptual change.

Mental models for operational domains

The various SSS involve different systems of operations. Students must acquire model templates for these systems to facilitate their use as knowledge extraction mechanisms. For example, it would be useful for the operation of the categorical system to have a template for classification and categorical relations between concepts, such as logical multiplication or tree diagrams. These models would help the student realize that the properties of objects intersect variably and that concepts are defined by these intersections. Thus, it would be useful to search for these intersections according to the template and fill it in according to the field in question, such as biology, physics, chemistry, etc. Moreover, they should understand that the template would be useful for reflection and elaboration on the relations between the objects and for subsequent recall and use. For example, in the phase of dual representations, preschoolers may be instructed to sort objects into piles according to various properties, such as shape or color, and tag them with names or other symbols. In primary school, they may be instructed to cross-classify according to two or more dimensions and elaborate on the logical relations implied by the classes constructed.

In a similar vein, it would be useful for the understanding of causal relations to have a template for their representation and manipulation. This template would involve the basic relations of causality (i.e., necessary and sufficient, necessary but not sufficient, neither necessary nor sufficient, and incompatible). Moreover, it would have to involve a demonstration of the basic tenet of causal modeling in science that covariation or correlation does not necessarily signify a causal relation. This is all the more important

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because correlation is taken as a sign of causality from very early in infancy (Keil 2011) and is part of everyday misconceptions of causal relations (Vosniadou 2008). A model of these relations would be helpful in directing the student how to manipulate the relations between factors in order to explore them and to interpret the results of experimentation. Moreover, the basic principles of the manipulation of causal relations, such as techniques and methods for the isolation of variables in different contexts and different knowledge domains, may be associated with the template. In primary school, children may be induced to single-factor experiments where one variable is varied along several levels to build a clear understanding of the relation between these variations and different effects. In adolescence, models for multifactor experiments may be taught where control requires matching between variables. Moreover, these models may be integrated with hypotheses and related deductive reasoning schemes.

In the quantitative domain, a model of the mental number line as early as preschool, a model of relations between different versions of mental number line in primary school, or models of different numerical operations would be useful for problem solving in mathematics. Also, models of different types of numbers, such as natural or rational numbers and the symbol systems used to express and operate on them, would be useful for learning mathematics. Finally, models of more complex notions, such as the notion of functions or algebraic relations, should be part of the teaching of mathematics at the end of primary school and in secondary school.

In the spatial domain, models of the three-dimensional space or episodic sequences in space may help represent the objects or relations concerned. It may be noted that training in visualization may be helpful. In clinical psychology, there is a vast literature in the use of mental imagery in cognitive behavior therapy. This literature highlights the techniques that may be used to educate students in constructing and using mental images for the sake of acquiring control of their behavior (Crits-Christoph and Singer 1981). Along this line, mental imagery is extensively used in the coaching of athletes. Visualizing the behavior under training beforehand facilitates performing the behavior as required (Jones and Stuth 1997). Also, the role of mental imagery in creativity and discovery is well known. Einstein’s thought experiments about the appearance of objects and relations when we approach the speed of light, Kekule’s model of the benzolium, and Watson and Crick’s three-dimensional model of the DNA helix are some of the very famous examples of how mental imagery may be used to envisage and better explore the implications of relations between concepts and entities (Finke 1993; Reed 1993).

Conceptual frames and conceptual change

Students, when encountering new concepts or problems, must specify the broad field of knowledge the concepts or problem belong to (e.g., physics, biology, psychology); the particular domain within a field of knowledge (e.g., gravity or force within physics, evolution or heredity within biology); the type of procedures and operations required (e.g., interpretation, categorization, measurement or estimation, hypothesis testing or explanation); and the special algorithms required (e.g., recall from memory, special sorting actions or special mathematical operations required). They must also specify what they do not know about the concept or problem. They must then try to remove all unknowns by extrapolation from what they know. Extrapolation can initially occur by induction and analogy that generate mental models based on similarities between what is known and what needs to be specified. Constructing mental models about possible interpretations or solutions to problems is important in this process because mental models are the means to go from what is known to what is not

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known. In constructing new mental models, students should understand that they may have to map new knowledge onto extant knowledge by combining, interweaving, or fusing new with old knowledge. Alternatively, their new model may just be a differentiated or refined version of an old model (Demetriou and Raftopoulos 1999; Raftopoulos and Constantinou 2004). The mental models may be visual, verbal, mathematical, or anything else. Of the models generated by induction and analogy, not all are useful or relevant. Redundant, inappropriate, or irrelevant models may be eliminated by deductive reasoning which helps evaluate them on the basis of the constraints of the field or domain accepted as the basis of understanding and problem solving. That is, the models that fit the constraints are lifted to the level of solutions or new concepts and those that do not fit are eliminated.

In this process, metarepresentation comes into play to abstract reasoning patterns and makes them available for future use. Thus, metarepresentation operates on two levels: At a first level, it stabilizes the models selected and encodes them as solutions to be called upon in the future. At a higher level, it lifts the very evaluation process into increasingly more flexible inferential schemes, such as those leading from simple modus ponens reasoning to the flexible reasoning enabling one to deal with fallacies.

Therefore, students must understand that knowledge is generated in the context of social institutions that exist for this purpose. These institutions are to a large extent the products of their time, they may coexist, and they may often provide conflicting answers to the same questions. An example is religion and science. Religion is much older. Science came very late in the history of civilization and is primarily identified by the logic of systematic inquiry and control of knowledge rather than by knowledge as such or belief. However, even in science, disciplines differ in their methods of inquiry, the answers they generate, and the objectivity and precision of the controls they employ to validate knowledge. In Kagan’s (2009) terms, humanities, social sciences, and natural sciences are the three cultures of knowledge. Engineering may be added as the realm of technical applications that can be derived from the various disciplines for the sake of particular social, cultural, or economical needs. The arts may be added as a special way of representing and interpreting reality, according to acceptable aesthetic criteria, in order to convey messages with special value for both the artist and society.

Students must understand that knowledge and concepts about the world are constrained by the particularities of the discipline that generated them. Therefore, students must develop mental models and templates appropriate for capturing how disciplines differ in using observation, controlled experiment, logical analysis, and argumentation for the construction of knowledge in their respective fields.

They must also understand how knowledge in each discipline is affected by the political, cultural, and historical contexts in which it was developed. Finally, they must develop a systemic approach that enables them to see each model from the point of view of others. For instance, although causal relations are everywhere the same, causation is expressed differently in physics, biology, human relations, and history. Specifically, in the various knowledge domains, cause–effect relations are expressed on different timescales (e.g., milliseconds in chemical reactions vs. thousands of years in evolution vs. billions of years in astronomy), take place on different levels of reality (e.g., observable reality vs. hidden reality that is not accessible to the senses), they may be masked by different kinds of confounding factors, and they thus require different manipulations to become obvious and established. Mental images have a different role in art (they may symbolize realities only remotely related to the image as such), geometry (analogical to the object of interest), natural science (closely related as representations of the realities of interest), and human interactions (laden with personal meaning and emotions). Quantitative thought is the mental

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background for mathematics, but it may not be equally applicable to express knowledge in physics, economics, psychology, and history. Thus, students must understand that operations in the various domains are expressed differently in different knowledge domains. As a result, their modeling is the same at one level of analysis (i.e., the fundamental causal relations are the same everywhere) but different at another level (i.e., content and form of interactions between factors). Moreover, educating students to translate between symbolic systems may be an excellent means for enabling them to explore the boundaries between domains, similarities and differences between mental operations within and across domains, and generate general inference patterns.

We suggest that the construction of new knowledge and skills and the epistemological awareness that is necessary may well be served if organized around the big ideas that were elaborated in different disciplines through the years. For example, ideas such as gravity, energy, or force in physics, evolution, heredity, or homeostasis in biology, or intelligence, motivation, or consciousness in psychology are good examples of ideas that can be developed in the curriculum. Each of these ideas may be presented from several perspectives. For example, they may be presented along a dimension that varies from their relevance to personal experience and everyday life to abstract models about underlying general laws that are remote from personal experience and everyday life. The historical perspective is also important. Explicating how an idea was conceived in different phases in the history of a discipline may enable students to realize that the same phenomenon may be differently understood over time as knowledge is tested and refined. Finally, the same idea may be discussed from the point of view of different disciplines. This would highlight both the differences between disciplines in their knowledge production mechanisms and also the multiplicity of representations that may exist for the same aspect of the world. The reader is reminded of the example of motion explicated above. Linn and Eylon’s (2011) knowledge integration model for science teaching (i.e., building on personal knowledge and ideas, using evidence to distinguish between alternatives, reflecting on alternative accounts of scientific phenomena) is fully compatible with our theory. As Smith (2009) has noted, this approach brings back Piaget’s epistemological orientation to knowledge construction as a guiding frame for educational science and practice.

The systemic approach advanced here requires a multidimensional representation of concepts that would enable the student to see how different elements or processes of the phenomenon concerned depend on and affect each other. For example, representing (1) the structure (e.g., what does it consists of, such as the wings of birds or airplanes); (2) the function (what is it for, such as for motion); and (3) the process (how does it operate, such as the flying movements of the birds or propulsion by the spinning of propellers or air emission of jet engines) of the phenomenon of interest enables students to grasp the relations both within and across concepts and disciplines and, when working on a problem, plan actions accordingly. Interestingly, several scholars proposed variants of these schemes as a means for guiding learning and problem solving in fields as different as engineering (Kalyuga et al. 2010) and biology (Hmelo-Silver and Pfeffer 2004). Kalyuga and Hanham (2011) have recently shown that a hierarchical exposition of how systems operate is more effective in producing learning and transfer. This is a general-to-detail instructional sequence where general ideas are presented first for each of the three aspects of the tripartite structure and are gradually differentiated into their specifics. However, it needs to be noted here that the effectiveness of the general-to-detail or the specific-to-general sequence may differ according to developmental level and expertise (Ericsson and Lehmann 1996).

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Educating inference and reasoning

We suggested above that development and learning within each of the domains does not automatically or necessarily generalize to the other domains. At the same time, the domains provide the background material for the activation and operation of metarepresentational processes that generate general reasoning patterns and problem-solving strategies that generalize beyond the domains. It is important for education to capitalize on these processes and direct the students to swing between the domain-specific and the domain-general operation as efficiently and profitably as possible. Below, we outline the framework that may be conducive to the attainment of this goal.

Education is directly or indirectly a process that gradually transforms automatic reasoning into controlled reasoning (Nisbett 2009; Olson 2003). However, this process can also be systematically educated. The theory and research discussed here suggest that the systematic education of reasoning should be directed to attain the following goals: (1) decontextualize inference; (2) differentiate between inferential processes and logical forms, such as inductive, analogical, and deductive reasoning; (3) make use of mental models for the sake of inference and reasoning; and (4) make use of metarepresentation to elevate reasoning from the handling of possibilities based on mental models to the mastering of the underlying logical relations and the implications of inference. This is an important educational goal because critical thinking would remain incomplete and impaired if the evaluation of information and arguments would be incomplete due to flaws in the analysis of their logical status and cohesion. Inductive and deductive reasoning may be the focus of teaching. Here, we will focus on deductive reasoning.

Deductive reasoning develops from a state where everything but content is implicit to a state where all crucial processes gradually become explicit (see above). Following the scheme outlined above, decontextualization of deductive reasoning would have to lead children to see the logical form of an argument beneath the content. An example is given below:

Birds fly. Birds fly. Birds fly. Bs do x.

Sparrows are birds. Ostriches are birds. Elephants are birds. As are Bs.

Therefore:

Sparrows fly. Ostriches fly. Elephants fly. As do x.

In this example, all four arguments are valid. However, in the first argument, both the premises and the conclusion are true. In the second argument, the premises are true but the conclusion is false. In the third argument, the second premise and the conclusion are false. Finally, the formal structure of the argument is shown.

At preschool, children may be led to realize that the information in the premises is connected by inference. Actual models of the organisms involved and visual representations of the line of inference going from the one to the other are obviously useful. At primary school, directed comparisons across the various arguments would enable children to differentiate form from content and understand that logic constrains inference. That is, when children understand that the conclusion “elephants fly” necessarily follows from the premises, given that we accept that “elephants are birds” and that “birds fly,” they already know that logical structure underlies inference and that content is irrelevant for the conclusion. For this to be possible, children must focus on the logical form of each sentence as given in the argument and ignore any other previous knowledge or information related to the meaning of the words in the sentences. Also, they must understand that in order to grasp

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the logical relationships implied by a logical argument, they need to break down or analyze the argument into the premises involved and focus on their logical or formal relationships independent of content. The formal relationships implied by the premises in an argument are normally hinted at or suggested by particular words of natural language, such as class indicators “is,” “are,” “and,” the conditionals “if … then”, the disjunctives “either … or,” etc.

In adolescence, the main aim of education would be to make metalogic explicit so that the adolescent understands that logic constrains the relations between statements and possible mental models and logical relations are specifiable through systematic search of an argument’s space. Thus, instruction should explicate what mental models follow from different types of relations and how all models implementing the possibilities associated with a particular relation are expressed by the relevant logical principle. For example, the simple modus ponens argument “if p then q,” combined to other premises, implies the following possibilities and truth functions: “if p then q” and “p,” then the conclusion “p and q” is compatible and true; “if p then q” and “not p,” then the conclusion “not p and not q” is compatible and true; “if p then q” and “not p,” then the conclusion “not p and q” is incompatible and indeterminate; and “if p then q” and “p,” then the conclusion “p and not q” is incompatible and false. In this way, controlled reasoning is gradually extracted and consolidated from automatic reasoning.

In our laboratory, Christoforides (2010) has shown that this approach is very successful in raising the reasoning capabilities of primary school children. Specifically, third and sixth grade children were systematically instructed to do the following: (1) interpret the premises analytically and differentiate them from the conclusion; (2) differentiate between necessary and likely conclusions; (3) spot contradictions between premises and conclusions and construct alternative models for premises and conclusions; and (4) recognize logical necessity and logical sufficiency. Although all children were able to profit from this program and become able to successfully deal with fallacies, children with higher working memory profited more. Moreover, children that were able to describe the logical similarities of arguments were also in advantage to gain from the program. Therefore, both processing efficiency and metacognitive awareness are conducive to educating logical reasoning.

Kuhn (1991, 2009) suggested that there are several skills that would be very important for sound reasoning in addition to decontextualization. Specifically, resisting certainty about conclusions, withholding judgment until examining all relations involved, and awareness that one’s own personal biases may influence judgment are important because they enable the thinker to be exhaustive, objective, and systematic. Finally, it is important to note that reasoning is a means in the service of information exchange and evaluation in human communication (Mercier and Sperber 2011). Therefore, education for reasoning must be associated with education for argumentation from multiple perspectives (Kuhn 1991).

Wrapping it up: education for problem solving

The discussion above focused on the education of the various systems involved in the problem-solving loop. Here, we focus on the education of the loop as such. Problems may vary enormously in complexity, in required systems, and in aim. Moreover, the priorities and type of problems that are of concern to individuals vary extensively with development. For example, in infancy, tool use, such as eating tools, or building behavioral skills is important. In youth, life choices, such as choosing what to study, accepting a job offer, or getting married, are important. Later on, designing a new product, advancing theory in a particular discipline, or making a political decision that would lead a country out of a crisis

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may be important. In all phases, however, problem solving shares common elements that may be educated.

Specifically, problems require a clear conception of the final goal. Also, they require the formation of a solution plan that entails the specification of a sequence of steps until reaching the goal. Thus, education for problem solving must enable the individual to realize the value of withholding action until formulating an action plan. Tool use, such as using a spoon for eating, is useful for educating infants to understand how different actions (e.g., different ways of holding the spoon) may influence attainment of the final goal (food in the mouth). Constructions that require replicating a model, such as building a tower, are useful for showing the value of planning and the importance of sequencing steps in the proper order (Keen 2011). Later on, in primary school, problems requiring to transform one state into another, such as the Tower of Hanoi (Fireman 1996) or the Tower of London (Albert and Steinberg 2011), are good examples.

Planning may also profit from working on problems with multiple solutions or impossible problems. Specifically, working on problems which have multiple solutions enables children to shift between different ways of representing the final goal (the outcome space of the problem) and different methods for reaching the goal (the methods space). Tsamir et al. (2010) showed that even kindergarten children can be educated to conceive of multiple solutions in simple mathematical problems (e.g., imagining alternative ways of equally distributing a set of objects in two equal subsets). Stein and Burchartz (2009) presented a series of impossible geometrical puzzles of increasing complexity. These problems force students to consider alternative goals or alternative solution plans, evaluate them against each other, and establish logically the relations between alternative solutions or the impossibility of reaching a solution.

In secondary school, students must learn that problem solving in real life often involves hierarchies of problems that are embedded in each other so that a major or a final aim may be achieved (Jonassen 2000, 2003). For example, a strategic problem (e.g., reduce pollution in an area) may require solving a series of complex understanding problems (e.g., about air circulation, energy transfer, etc.), and each of these may require solving domain-specific reasoning problems (e.g., causality relations, mathematical estimations of relations, categorization of effects, etc.). Once solved, the problem may be transformed into a policy problem (e.g., how to change customs and behaviors in the society) and a story problem (e.g., how to convey the message about these changes). The students must be given the opportunity to think on and assemble complex hierarchical problems where hierarchies are embedded into each other. Working on problems of this kind may enable students to differentiate between the various cognitive systems and processes activated in problem solving (e.g., representing goal stacks, building relevant mental models, calling upon knowledge from memory or books, evaluating models and knowledge by reasoning, choosing between solutions, etc.).

Educating the self-awareness loop

Learning to learn requires awareness and control of the learning process so that it takes place at the right pace, it is geared on the demands of the task at hand, it can bypass possible processing and representational limitations by properly arranging the material to be learned, it judiciously uses relevant prior knowledge to enhance new learning, and it ensures that learning will endure. Therefore, at a basic level, learning to learn requires understanding of the role of the fundamental knowledge extraction and processing mechanisms. Namely, that control of attention is crucial for intake of relevant information, that rehearsal and association are crucial to establish the necessary connections and protect

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information from forgetting, and that self-testing is important to check if learning did occur. Learning to learn also requires an awareness of motivational and personality dispositions which are related to learning and relevant self-regulation skills that would maximize support and minimize hindrance to learning. Below, we will elaborate on interventions directed to improve knowing one’s own mind and to the use of assessment for the sake of learning to learn.

Learning to learn: knowing one’s own mind and dispositions

Learning to learn may be enhanced if students understand the organization and functioning of the mind. This would enable students to differentiate the handling of information coming from different systems. For example, mental images are more easily represented and preserved in working memory than abstract expressions in mathematics (Rasmussen and Bisanz 2005). Therefore, more rehearsal may be needed to process and remember mathematical representations as compared with mental images. Moreover, if students understand that all domains deliver their representations to a common limited representa- tional space, they may become able to notice factors in the target concept, such as the organization or quantity of information, that cause difficulties in its representation and processing. For instance, realizing that working on too many relations at a time may jeopardize understanding might enable the student, if necessary, to reorganize and re-chunk information so that relations are gradually constructed within the range of one’s own representational and processing capacity. Moreover, if students have an accurate self- representation of their own strengths and weaknesses in concern to different domains, they may be able to differentiate their strategies for handling learning according to domain. For example, being strong or expert in a domain allows for faster processing of larger chunks of information than in domains where one is weak, which would require slower processing and more rehearsal (Gobet 1998).

Education for learning to learn should be adapted to the needs of different developmental phases. Infants are not explicitly aware of learning and mental processing. Therefore, educating learning to learn in infancy can only be indirect, aiming to make the infant realize that different approaches to solving a problem may lead to different information. For example, leading the infant to compare alternative ways of using a particular tool, such as a key, may show her that each way has a different implication, such as hindering or opening access to a different view or different objects. Imitation may be a powerful means for learning to learn at this phase. For instance, comparing one’s own and an adult’s way of using a tool and then imitating the adult may raise the infant’s awareness of her own behavior and enhance self-monitoring and self-regulation as such.

Preschoolers have a limited representational capacity, a limited understanding of representation as a means for bypassing representational capacity limitations, and a limited command of representational aids, such as writing and note taking. This makes the learning of complex tasks at this phase of life very difficult. Some of the difficulties may be ameliorated if the children are aware that using representational aids may compensate for the other difficulties. For example, when working on a task with multiple solutions (e.g., Tsamir et al. 2010), preschoolers may be instructed to use scripts (e.g., specific signs on a paper) or real objects as tokens of the solutions generated up to a point. This approach, in addition to facilitating learning and problem solving as such, enables children to know the advantages and limitations of representation as a mental modeling function. Also, it brings to focus metarepresentation as the represen- tation (e.g., the signs or token objects used) of representations (each solution) that can

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help one handle the difficulties of the problem-solving or the learning process. Obviously, this approach is useful for later phases as well.

In line with the approach above, Zelazo (2011) suggests that mindfulness training from preschool years enhances self-monitoring skills which in turn enhance executive control thereby improving learning. Mindfulness training at this age would involve raising awareness of self by systematically shifting attention to different sensory experiences, such as visual and auditory, and examining how they affect thought. For example, when a child looks at apples she thinks about apples and when she looks at pears she thinks about pears; however, when thinking about the mother while looking at apples, she may not “see” changes in the number of apples and thought may alternate between the mother and the apples. Flook et al. (2010) showed that training of this kind improves self-regulation and executive control significantly, especially in children who are not strong in this regard.

Primary school children still do not clearly differentiate between cognitive functions, nor do they understand the learning effects of different cognitive functions or activities. For instance, at this age, they may be deceived that what is in their eyes is also in their mind. Likewise, they may be deceived that what is in short-term-memory is also in long-term memory. That is, they think that they will remember something later on and be able to recall it simply because they understand it when it is in front of them or when they have just formulated a representation of it (Flavell et al. 1995, 1997). It is to be noted that these weaknesses coincide with the years of primary school that is a period of intensive formal learning of new concepts and skills, such as reading, writing, arithmetic, science, etc.

Therefore, at primary school, education must focus on building awareness of the differences between mental functions and of their differential impact on learning. Moreover, it must focus on bridging activities with different mental functions and aspects of learning. For instance, children must realize that recall of information from memory and its connection with what is in front of their eyes facilitates understanding of new information. In turn, they must realize through practice that rehearsal facilitates storage of new information in long-term memory for later use and that this may be tested by asking questions to themselves (Dehn 2008). Building associations and relating new with prior knowledge helps learning, but variation and differentiation helps originality in problem solving (Glassner and Schwarz 2007; Siegler 1996; Simonton 2000).

In adolescence, self-awareness gradually becomes process-driven and executive inhibition is intertwined with the suppositional stance associated with conditional representations. This allows problem solving to become planful and systematic. Moreover, the self-concept becomes dimensionalized and generally accurate. By middle adolescence, students start to differentiate between the cognitive processes involved in different kinds of knowledge and activities, such as mathematics vs. physics, they know where they are strong at and where they are weak at, and they can plan their problem-solving activities from the start so that they can seek information when and where it is needed and integrate it into their current problem-solving endeavors (Demetriou 2000; Demetriou and Kazi 2001, 2006).

Education for learning to learn at this phase should focus on awareness of the differences between cognitive domains in the mental processes they involve and in how they relate to their world domains. That is, the individual should be able to recognize and understand the formal relations between constructs, in the first case, and evaluate the consistency between a model and reality, in the second case. For example, they may be guided to grasp the differences between formal disciplines, such as mathematics, and empirical disciplines, such as physics, in knowledge construction. Therefore, planning requires different approaches in the two cases. In the first case, it requires exhaustive search of the logical relations between premises and presuppositions. In the second case, it requires a conception

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of the world as suggested by a model, the specification of the steps needed to produce crucial evidence, and the realization of these steps through the necessary actions.

Finally, students must understand that it is better for future performance and learning to metarepresent the processes activated or the solutions reached. On the one hand, science is a vast universe of representations about the world and metarepresentations specifying what the representations mean, why, how, and when they may be changed (van Fraassen 2010). On the other hand, classrooms are representational and metarepresentational laboratories. One of the aims of education is to gradually bring the representational and metarepresentational systems of the individual as close as possible to the representational and metarerepresentational systems of science. This is a double-faced process. On the one hand, inducing students to the representational and metarepresentational nature of science facilitates domain-specific learning. For example, Nesher and Sukenik (1991) showed that the learning of mathematical relations (i.e., ratios and proportions) is most enhanced when the learning experiences provided in the experiment were paired with relevant formal representations. These representations enabled the subjects to summarize and manipulate their learning experiences. On the other hand, it enhances the development and flexibility of general patterns of thought and the development of a general modeling ability that is associated with a general LOT because it enables students to work out the relations between different representational systems and metarepresent them (diSessa 2004; Yerushalmy 1997). In Carey’s (2009) terms, newly acquired symbols function as placeholders which are gradually filled in with meaning through the mechanisms discussed above. To support metarepresentation and facilitate the emergence of general problem-solving strategies and reasoning patterns from domain-specific learning, teaching must continually raise awareness in students of what may be abstracted from any particular domain-specific learning. Moreover, students must be facilitated to map representations from different domains onto each other and tag new constructs with new representations that will enable them to mentally recall, manipulate, and relate the new representations with others already in existence (Dixon et al. 2010).

Assessment for learning and learning to learn

Assessment in education is crucial for learning because it informs the learner for possible divergences between learning goals and outcomes. It is beyond the aims of the present article to embark on the vast literature on assessment in education. It is to be noted, however, that theories of cognitive development are recognized as potentially useful frames for the development of assessment methods that would be valuable for learning and instruction. Unfortunately, however, very few attempts have been made to highlight how cognitive development theory and research can inform educational assessment (Ercikan 2006). The model of mind proposed here offers a full framework for assessment that would be very useful for learning in the school.

Here, we try to show how assessment in education can facilitate students to (1) enhance their knowledge of their own mind; (2) sharpen their self-monitoring, self-representation, and self-regulation skills; and (3) make them aware of their strengths and weakness in the various components of the mind and the different domains of knowledge. These provisions are in line with the aims of formative assessment or assessment for learning, which aims to make students owners of their own learning (Black and Wiliam 2009). In terms of the present theory, assessment must first of all enable the students to recognize the sources of difficulty in understanding a concept or solving a problem and assist them in developing strategies for overcoming them. The ultimate aim is to make assessment a powerful tool of metarepresentation.

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Difficulty may arise from any component of the architecture of mind proposed here. For example, students evaluate or tag problems or concepts as difficult if they cannot make sense of them in reference to prior relevant knowledge. Difficulty also arises from conflicts or inconsistencies between the concepts to be learned and those already possessed. Assessment here must highlight what is lacking and direct the student to search for necessary knowledge and use it for the sake of the problem at hand. Search must be both self-directed (Do I know something similar? Have I solved problems like this in the past?) and directed to external sources of knowledge (asking others, searching for similar problems in books or others sources, etc.). In the case of conflict or inconsistency between concepts, assessment must direct the student to notice the superiority of the new concepts to explicate the phenomena of interest. For example, why is the heliocentric model of our planetary system superior to the geocentric model.

Problems are also difficult for students if the amount or the presentation rate of information exceeds the available representational or processing resources. Assessment here must make the students sensitive to their representational and processing limitations. Exposing the students to a range of concepts or problems of varying complexity or presentation rate in order to make them sense the “personal point of command” can serve this purpose. This kind of assessment must be associated with instruction directed to build the necessary skills for bringing the problem back to the personal point of command. These skills are related to the building of the executive processes mentioned above (Brown 1987).

A third source of difficulty resides in the inferential processes themselves. That is, if the concept or problem to be dealt with involves relations that require inferential schemes that are not available, such as dealing with the fallacies, the crucial relations will not be worked out. Assessment here must highlight the inferential schemes missing and practice the student in their use.

Finally, difficulty and limited success in learning may come from inaccurate self- evaluation of performance. As shown above, self-evaluation of problem solving is moderated by a personal constant that is characteristic of the individual and is stable through the years. When inaccurate, self-evaluation may cause students to abandon a learning task because it is judged too difficult or terminate an attempt because it is unjustifiably judged to be successful. Inaccurate self-evaluations may even deceive the teachers themselves, thereby hindering them to provide to the student the assistance needed for a learning task. Therefore, assessment for learning to learn must help the students calibrate, so to speak, their personal constant to their actual ability and performance in a given domain. This calibration would require tasks where the students are asked to evaluate their performance and compare their evaluations with the evaluations of their teachers and other students (Harter 1999). The aim would be twofold in this regard. On the one hand, the students must acquire awareness of their overall self-evaluation trends: i.e., if they tend to be lenient, strict, or accurate. This awareness should include the cognitive (e.g., impulsivity and limited planning, limited or inappropriate mental modeling for solutions, lack of domain-specific criteria or standards for what is right and wrong, etc.) and motivational and emotional aspects of self- evaluation (e.g., fear of failure and criticism, anxiety, avoidance, etc.). On the other hand, students should acquire the self-monitoring and self-regulation skills that would improve the accuracy of their self-evaluations. The suggestions advanced above about educating the various aspects of problem solving and critical thinking are relevant here. Understanding the long-term value of self-evaluation for social standing and emotional stability must be part of education for accurate self-evaluation.

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Critical thinking, epistemological awareness, and creativity

Critical thinking has been a privileged topic of discussions in pedagogy and philosophy of education since a long time ago (Ennis 1962, 1996; Siegel 1988, 1989a, b). The main line of argument is that the development of critical thinking must be one of the main aims of education. Definitions vary, but there is consensus that critical thinking involves inference, recognition of assumptions, deduction, interpretation, and evaluation of arguments. Critical thinkers exhibit open-mindedness, tolerance of ambiguity, and a skeptical, questioning attitude (Bernard et al. 2008). Moreover, critical thinking includes the ability to (1) identify central issues and assumptions; (2) envisage alternative models; (3) associate each model with its own supportive evidence and logical substantiation; (4) embed each model into its own conceptual or belief system; (5) identify logical flaws in arguments, descriptions, or explications; and (6) adopt an informed preference based on evidence or argumentation (e.g., Ennis 1987; Furedy and Furedy 1985; Pascarella and Terenzin 2005; Watson and Glaser 1980).

From the point of view of the present theory, conceptual change, reasoning development, learning to learn, and critical thinking are complementary aspects of cognitive development and learning. Specifically, the very process of constructing and evaluating mental models for the sake of grasping target concepts and problem-solving skills may become a powerful field for the development of critical thinking. Envisaging, comparing, substantiating, debating, and choosing among alternative models, if properly directed, may enhance awareness of the role of evidence and logical and conceptual substantiation for alternative models. Siegel (1989a) suggests that for this to be possible, critical thinking must be embedded in a context of epistemological understanding. This is so because critical thinking presupposes particular answers to important epistemological questions (e.g., what is truth, what is evidence, what is reason or cause, what is more important, reason or evidence etc.). These questions are systematically answered in the context of different fields of knowledge, and thus the thinker can be critical if she is aware of the answers given to these questions so that she knows how and where to frame her analysis and arguments. Thus, epistemology is basic both to critical thinking and to its pedagogy. Understanding of the fundamental epistemological assumptions of the three main realms of human knowledge—that is, natural sciences, social sciences, and humanities—becomes a condition for critical thinking.

Educating for critical thinking must take into account the strengths and weakness of successive developmental phases. Thus, we propose that teaching for critical thinking must proceed by capitalizing on the worldview that is prevailing during each developmental milestone. The ultimate aim would be to enable the developing person to realize the knowledge, understanding, and decision-making limitations of each phase and gradually build up a comprehensive approach to knowledge acquisition and problem solving where information processing is a means to the end of understanding, reasoning is a tool for evaluation and analysis, and rationality is integrative judgment that bridges multiple frames and points of view (Jacobs and Klaczynski 2002).

In preschool, children must overcome the absolutist stance of this age phase that knowledge is either right or wrong. An example would be to make them realize that the knowledge of all three different children about an object (e.g., that it is red, and green, and blue) is right, depending on each child’s perspective (i.e., because each of the three sides of the object is differently colored). In primary school, children must realize that the representations associated with each perspective are integrated by inference which gives them coherence and logicality, given the initial assumptions. Moreover, they must be

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gradually introduced to an epistemological approach to knowledge that would allow them to understand that the knowledge generated by different knowledge extraction mechanisms, such as observation and experiment, may differ in accuracy and validity, depending upon how well confounding factors are controlled by each. The adolescent and the college student must recognize that alternative approaches to knowledge, such as the three major realms of knowledge, or different epistemological traditions about knowledge, such as realism and relativism, may be equally acceptable depending upon the person’s original stance to knowledge. For example, one is a realist if one believes, as scientists do, that there is truth about the world which can be approached through appropriate scientific methods. Distance from truth is only a matter of appropriateness and precision of methods and ingenuity of controls, which they improve with accretion of knowledge in time. One is a relativist if one believes that there is no single truth about a phenomenon and knowledge is a function of approach, perspective, or conviction. Adolescents must gradually become familiar with the fact that one’s epistemological stance defines one’s interpretation and even handling of knowledge (Chandler et al. 2002; Wildenger et al. 2010). Moreover, critical thinking includes the skills necessary for the students to know themselves so that they orient themselves to the directions that are most suitable not only to them but also for their group. Critical thinkers are the citizens who can make choices for their own benefit and the benefit of society. Obviously, nothing can compensate for actual experience, but studying cases and alternative scenarios might be helpful in increasing awareness about the value of logic, knowledge, experience, and learning for wise and useful decision making.

Creativity is a fast developing area of research and educational practice (Runco and Pritzker 1999; Sternberg 1999, 2005), which is not the focus of this essay. It needs to be briefly associated, however, with the present theory. From our point of view, creativity comes at the intersection of the problem-solving and the self-awareness and self-control loops and requires critical thought as a tool. Specifically, creativity is an approach to problem solving that systematically aims for solutions that are different from and/or better than earlier ones. This requires that problem-solving goals are contrastively specified in reference to earlier solutions so that a new solution integrates additive advantages or differences relative to earlier ones. In turn, this requires embedding problem solving in (1) a social and cultural context, where solutions are evaluated for their use and usefulness, and (2) a historical and epistemological context where advantages and disadvantages of solutions are specified relative to the historical, social, cognitive, and technical constrains related to their production.

Under this perspective, education for creativity is a long-term process that co-extends with the course of development described here. In infancy, education must enable the infant to see that alternative solutions to a problem may differ in their usefulness and completeness relative particular needs. In primary education, education may start focusing on the planning process of problem solving in order to enable the child to see that variations in the specification of the goal and the relative mental modeling required to plan solutions may lead to different solutions. That is, education for creativity must be an integral part of the education of problem solving as described above. In adolescence, in addition to refining problem-solving processes as above, education must familiarize the adolescent with how creativity may be practiced in different fields. That is, that in addition to the common processes that are required for creative accomplishments, different fields may have different standards or criteria for what is a creative solution or idea. In college, education should enable the students to relate their creative endeavors with the value and relevance of solutions vis-à-vis their historical, cultural, social, economic, technological, and disciplinary contexts. Finally, education for creativity must enable the person to value cooperation with

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other persons as conducive for creativity and aesthetic criteria as important for the quality and acceptance for the solutions to be produced.

Conclusions

We attempted to show that an educationally useful theory of the developing mind is possible. This theory integrates constructs from developmental, cognitive, and differential psychology. The main postulates of the theory can be summarized as follows:

There are general mechanisms of intelligence. These include general processing efficiency functions that enable humans to represent and process information, general inferential process underlying processing, and self-awareness, self-regulation, and reflection, directing and transforming processing. General mechanisms and possibilities coexist with, and are expressed through, a number of specialized domains of thought that capture different types of relations in the environment. Mental operations in the domains are knowledge extraction mechanisms whose products intermingle to generate knowledge domains, as we know them in the history of culture, science, and education.

These functions change with age, and their changes are reflected in changes in the quality of understanding and problem solving. In turn, these qualitative changes are expressed through particular world views that define a series of developmental milestones and are associated with an increasingly expanding language of thought. With age, there is a gradual shift of importance from knowledge representation and handling mechanisms to knowledge as such.

Individual differences in the state of the general efficiency factors and the condition of the domains cause differences in the rate of intellectual development both within and across individuals. Differences across individuals are reflected in psychometric measures of cognitive ability, such as the IQ tests. Moreover, these differences may come from differences in the predispositions or facility of different persons in the various domains. Differences in experience in different domains may be related to both intra- and inter-individual differences in performance and developmental rate.

Education can capitalize on this model. The main implications can be summarized as follows:

At any phase, education must lead the student to develop and refine the following cognitive skills: focus on relevant information; scan, compare, and choose according to goal; ignore irrelevant information; represent what is chosen and associate with extant knowledge; bind into models and rehearse if necessary; evaluate models in reference to evidence; reason by deduction to evaluate truth and validity of models and conclusions; prefer solutions that are better or nicer than extant solutions; estimate consistency with beliefs, extant theories, dominant views, etc; and encode, symbolize, and embed into the system. Being aware of epistemological issues concerning the similarities and differences between disciplines is an important part of education for critical thinking and creative thinking.

These general educational goals must be tuned to the developmental possibilities and constraints of successive developmental phases. The general rules that may govern this tuning are as follows: (1) Educational priorities must capitalize on developmental milestones to make education feel relevant, appropriate, and useful for each phase of life. (2) Organization and presentation rate of material to be learned must take representational capacity and processing efficiency into account in order to make learning possible and powerful at every phase. (3) Education must practice, consolidate, and build models for core and mental operators in each SSS because they function as knowledge extraction

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mechanisms; their efficiency influences the quality of information that is generated and fed into the system. With age, emphasis should shift from educating mental operations to knowledge as such. Students must be induced into theme- or concept-appropriate models of the fields of interest, such as gravity, inheritance, motion, etc. Teaching must lead the students to understand where and why these models are better than their own respective concepts or models. (4) In the process, special attention should be given to the lifting of inference from automatic (system 1) to analytic (system 2) functioning. Decontextualization of inference from content and context is important in this process. (5) Problem solving involves all of the processes above, and it should be demonstrated and practiced as such. Students must be educated in foresight, anticipation, and formulation of alternative plans for problems. Moreover, with age, knowledge and context are increasingly important for successful, acceptable, and creative problem solving. (6) Finally, students must know themselves: the organization and functioning of their own mind, their own strengths and weakness, and how to adjust their actions and learning accordingly.

One might object that our approach to education devolves into a readiness notion where nothing is taught on a subject until the mind is “ready” and that nothing is done to facilitate or accelerate development. In Piaget’s (1980) words, this is “the American question.” For one thing, in developmental terms, consolidating the constructions of each phase facilitates the earlier transition to the next phase. In psychometric terms, this approach augments g. Therefore, it makes learning self-expansive by definition. For another, our approach is person-centered in the sense that it supports the weaker students to build the capacities they are weak on and opens the way for the stronger students to capitalize on their strengths and move faster in the construction of new knowledge and skills.

It may be noted here that the gap in academic achievement that is observed in many countries between privileged and underprivileged children, such as the Black–White gap in the USA (Sackett et al. 2001), can be associated with lack of school readiness upon entry to school. Moreover, relative age effects are ubiquitous. They exist in every classroom and every grade. These influence the rate of learning in future grades, causing the gap to become increasingly bigger (Hunt 2011). Therefore, education designed on the basis of the principles proposed here may protect children, especially those at risk, from the problems they face later on when learning becomes more demanding. As it builds up, it frees students from their weakness because it strengthens intelligence as much as knowledge and learning.

The so-called Flynn effect is the systematic increase in intelligence test scores over the last century. Flynn (2009) ascribed this increase to two main factors. The increasing pressure for abstract thought that is required by our increasingly symbolic technological environment and the increasing expansion of education. This is fully compatible with the present theory: These changes in human culture affect, unsystematically, the two main loops of intellectual functioning described here which underlie performance on intelligence tests. At the same time, we predict that systematizing education according to the theory presented here will further accelerate the Flynn effect.

Concerning general issues about educational policies and orientations, the model suggests that plain constructivism, which dominated in discussions about educational priorities since the 1980s, is not enough for efficient education. In addition to self-directed activity and discovery, guided abstraction, representation, and meterepresentation are very important for stable learning and learning transfer. Epistemological awareness and understanding of social and historical relevance of knowledge and knowledge production mechanisms are very important for the proper and constructive functioning of the student as a citizen. Therefore, education must induce the student into the origins, nuances, and possible effects of the historical, cultural, social, and disciplinary frames of knowledge.

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Teachers operate in a vast field of intra- and inter-individual differences. Therefore, curriculum, instruction methods, and moment-to-moment teaching must adapt to different students and to different subject matters. It is reminded that students who are weak in processing efficiency need more help and support to learn. The implications of this suggest the development and use of appropriate diagnostic tools and the continuous and effective education of the teachers. After all, democratic education must lead each student as close as possible to his or her potential across the board at successive developmental phases.

It is obvious that the realization of the educational implications of the theory discussed here requires extensive changes in a number of domains that are relevant but distinct from education as such. First, it requires the development of new curricula and teaching content in different school subjects that would match the priorities and goals discussed here, from preschool to senior high school and college. Obviously, this requires the support of policy- making officials, including governments, academic bodies, and educational administrators. Second, the education of the teachers themselves is very important. Teachers must understand the processes and dynamics of mental processing, development, learning, and intra- and inter-individual differences in all of these aspects of the mind in the same way that medical doctors understand the processes and the dynamics of human body, its chemistry and genetics, and its change over time. Therefore, programs of study at the university must be organized so that the relevant knowledge and skills are available to the teacher upon graduation. Finally, the advantages of modern technology, including computer, virtual reality, and Internet technology, must be fully employed and incorporated in the learning procedure. For instance, modern computers and virtual reality systems may enable students to have access to and study the complex phenomena that go beyond direct experience, such as the structure of matter, the DNA, or the Universe, that go much beyond anything that the educational technology of the near past could support. This technology can also be used to practice practically every skill and ability discussed here. Learning scientists, computer scientists, teachers, subjects specialists, and industry should cooperate to transform these possibilities into special learning environments.

Obviously, we have a long way to go until the vision of education drawn here becomes a reality in all of its elements. We hope that this essay will open a constructive discussion that will lead us close.

Acknowledgments Special thanks are due to the following colleagues for reading and commenting upon the ideas advanced in an earlier version of this paper: Lorin Anderson, Erik De Corte, David Olson, Howard Gardner, Jarkko Hautamaki, Earl Hunt, Karin Bakracevic, Mary Koutselini, David Moshman, Willis Overton, Robert Ricco, Gabi Salomon, Michael Shayer, Leslie Smith, Timos Papadopoulos, Stella Vosniadou, and two anonymous reviewers.

Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

References

Ackerman, P. L. (1996). A theory of adult intellectual development: Personality, interests, and knowledge. Intelligence, 22, 227–257.

Ackerman, P. L. (2000). Domain-specific knowledge as the “dark matter” of adult intelligence: Gf/Gc, personality, and interests correlates. Journal of Gerontology: Psychological Sciences, 55B, 69–84.

654 Educ Psychol Rev (2011) 23:601–663

Albert, D., & Steinberg, L. (2011). Age differences in strategic planning as indexed by the Tower of London. Child Development, XX, 1–17.

Anderson, L. W. (1985). Time and timing. In C. W. Fisher & D. C. Berliner (Eds.), Perspectives on instructional time (pp. 157–168). London: Longman.

Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., et al. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives. New York: Longman.

Artman, L., Cahan, S., & Avni-Babad, D. (2006). Age, schooling and conditional reasoning. Cognitive Development, 21, 131–145.

Baars, B. J. (2002). The conscious access hypothesis: Origins and recent evidence. Trends in Cognitive Sciences, 6, 47–52.

Baars, B. J., & Franklin, S. (2003). How conscious experience and working memory interact. Trends in Cognitive Sciences, 7, 166–172.

Baddeley, A. D. (1990). Human memory: Theory and practice. Hillsdale: Lawrence Erlbaum. Baddeley, A. D. (2000). The episodic buffer: A new component of working memory? Trends in Cognitive

Sciences, 4, 417–423. Baltes, P. B., & Staudinger, U. M. (2000). Wisdom: A metaheuristic (pragmatic) to orchestrate mind and

virtue toward excellence. American Psychologist, 55, 122–137. Barouillet, P., Portrat, S., & Camos, V. (2011). On the law relating processing to storage in working memory.

Psychological Review, 118, 175–192. Barrouillet, P. (1997). Modifying the representation of if … then sentences in adolescents by inducing a

structure mapping strategy. Current Psychology of Cognition, 16, 609–637. Barrouillet, P., Grosset, N., & Lecas, J.-F. (2000). Conditional reasoning by mental models: Chronometric and

developmental evidence. Cognition, 75, 237–266. Barrouillet, P., Gauffroy, C., & Lecas, J.-F. (2008). Mental models and the suppositional account of

conditionals. Psychological Review, 115, 760–771. Basseches, M. (1984). Dialectical thinking and adult development. New York: Ablex. Bernard, R. M., Zhang, D., Abrami, P. C., Sicoly, F., Borokhovski, E., & Surkes, M. A. (2008). Exploring the

structure of the Watson–Glaser Critical Thinking Appraisal: One scale or many subscales? Thinking Skills and Creativity, 3, 15–22.

Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21, 5–13.

Blair, C. (2006). How similar are fluid cognition and general intelligence? A developmental neuroscience perspective on fluid cognition as an aspect of human cognitive ability. The Behavioral and Brain Sciences, 29, 109–160.

Blaser, E., & Kaldy, Z. (2010). Infants get five starts on iconic memory: A partial-report test of 6-month-old infants’ iconic memory capacity. Psychological Science, 21, 1643–1645.

Braine, M. D. S. (1990). The “natural logic” approach to reasoning. In W. F. Overton (Ed.), Reasoning, necessity, and logic: Developmental perspectives (pp. 133–157). Hillsdale: Lawrence Erlbaum.

Brown, R. (1968). The development of Wh questions in child speech. Journal of Verbal Learning and Verbal Behavior, 7, 279–290.

Brown, A. L. (1987). Metacognition, executive control, self-regulation, and other more mysterious mechanisms. In F. E. Weinert & R. H. Kluwe (Eds.), Metacognition, motivation, and understanding (pp. 65–116). Hillsdale: Lawrence Erlbaum.

Buschkuehl, M., & Jaeggi, S. M. (2010). Improving intelligence: A literature review. Swiss Medical Weekly, 140, 266–272.

Butterworth, G. (1998). Origins of joint visual attention in infancy: Commentary on Carpenter et al. Monographs of the Society for Research in Child Development, 63(4), 144–166.

Camos, V., & Barrouillet, P. (2011). Developmental changes in working memory strategies: From passive maintenance to active refreshing. Developmental Psychology, 47, 898–904.

Campbell, F. A., & Burchinal, M. R. (2008). Early childhood interventions: The Abecedarian Project. In P. C. Kyllonen, R. D. Roberts, & L. Stankov (Eds.), Extending intelligence: Enhancement and new constructs (pp. 61–83). New York: Lawrence Erlbaum.

Carey, S. (1985). Conceptual change in childhood. Boston: The MIT Press. Carey, S. (2009). The origins of concepts. Oxford: Oxford University Press. Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York: Cambridge

University Press. Case, R. (1985). Intellectual development: Birth to adulthood. New York: Academic. Case, R. (1992). The mind’s staircase: Exploring the conceptual underpinnings of children’s thought and

knowledge. Hillsdale: Lawrence Erlbaum.

Educ Psychol Rev (2011) 23:601–663 655

Case, R., & Okamoto, Y. (Eds). (1996). The role of central conceptual structures in the development of children’s thought. Monographs of the Society for Research in Child Development, 61 (Serial No. 246).

Case, R., Okamoto, Y., Griffin, S., McKeough, A., Bleiker, C., Henderson, B., & Stephenson, K. M. (1996). The role of central conceptual structures in the development of children’s thought. Monographs of the Society for Research in Child Development, 61 (17–2, Serial No. 246).

Case, R., Demetriou, A., Platsidou, M., & Kazi, S. (2001). Integrating concepts and tests of intelligence from the differential and the developmental traditions. Intelligence, 29, 307–336.

Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54, 1–22.

Ceci, S. (1991). How much does schooling influence general intelligence and its cognitive components? Developmental Psychology, 27, 703–722.

Chandler, M. J., Hallett, D., & Sokol, B. W. (2002). Competing claims about competing knowledge domains. In B. K. Hofer & P. R. Pintrich (Eds.), Personal epistemology: The psychology of beliefs about knowledge and knowing (pp. 145–168). Mahwah: Lawrence Erlbaum.

Cheng, P. W., & Holyoak, K. J. (1985). Pragmatic reasoning schemas. Cognitive Psychology, 17, 391–416. Christoforides, M. (2010). The development of hypothetico-deductive thought in primary school children:

Implications of an intervention program (Unpublished doctoral dissertation). University of Cyprus, Nicosia, Cyprus.

Cleeremans, A. (2008). Consciousness: The radical plasticity thesis. In R. Banerjee & B. K. Chakrabarti (Eds.), Progress in brain research (Vol. 168, pp. 19–33). Amsterdam: Elsevier.

Cobley, S., McKenna, J., Baker, J., & Wattie, N. (2009). How pervasive are relative age effects in secondary school education? Journal of Educational Psychology, 101, 520–528.

Cole, R. L., & Pickering, S. J. (2010). Phonological and visual similarity effects in Chinese and English language users: Implications for the use of cognitive resources in short-term memory. Bilingualism: Language and Cognition, 13, 499–512.

Colom, R., Rebollo, I., Palacios, A., Juan-Espinosa, M., & Kyllonen, P. (2004). Working memory is (almost) perfectly predicted by g. Intelligence, 32, 277–296.

Commons, M. L., Richards, F. A., & Kuhn, D. (1982). Systematic and metasystematic reasoning: A case for a level of reasoning beyond Piaget’s formal operations. Child Development, 53, 1058–1069.

Conway, A. R. A., Cowan, N., Bunting, M. F., Therriault, D. J., & Minkoff, S. R. B. (2002). A latent variable analysis of working memory capacity, short term memory capacity, processing speed, and general fluid intelligence. Intelligence, 30, 163–183.

Cosmides, L., & Tooby, J. (1994). Origins of domain-specificity: The evolution of functional organization. In L. Hirschfeld & S. Gelman (Eds.), Mapping the mind: Domain-specificity in cognition and culture (pp. 85–119). New York: Cambridge University Press.

Cowan, N. (2010). The magical mystery four: How is working memory capacity limited and why? Current Directions in Psychological Science, 19, 51–57.

Crits-Christoph, P., & Singer, J. L. (1981). Imagery in cognitive-behavior therapy: Research and application. Clinical Psychology Review, 1, 19–32.

Dalke, D. E. (1998). Charting the development of representational skills: When do children know that maps can lead and mislead? Cognitive Development, 13, 53–72.

Daniel, D. B., & Klaczynski, P. A. (2006). Developmental and individual differences in conditional reasoning: Effects of logic instructions and alternative antecedents. Child Development, 77, 339– 354.

Davidson, M. C., Amso, D., Anderson, L. C., & Diamond. (2006). Development of cognitive control and executive functions from 4 to 13 years: Evidence from manipulations of memory, inhibition, and task switching. Neuropsychologia, 44, 2037–2078.

Dawson, T. L. (2006). Stage-like patterns in the development of conceptions of energy. In X. Liu & W. Boone (Eds.), Applications of Rasch measurement in science education (pp. 111–136). Maple Grove: JAM Press.

Deary, I. J. (2000). Looking down on human intelligence. Oxford: Oxford University Press. Deary, I. J., Strand, S., Smith, P., & Fernandes, C. (2007). Intelligence and educational achievement.

Intelligence, 35, 13–21. Dehaene, S. (1997). The number sense: How the mind creates mathematics. New York: Oxford University

Press. Dehn, M. J. (2008). Working memory and academic learning: Assessment and intervention. Hoboken: Wiley. DeLoache, J. S. (1991). Symbolic functioning in very young children: Understanding of pictures and models.

Child Development, 62, 736–752. DeLoache, J. S. (1995). Early understanding of the use of symbols. Current Directions in Psychological

Science, 4, 109–113.

656 Educ Psychol Rev (2011) 23:601–663

DeLoache, J. S. (2000). Dual representation and young children’s use of scale models. Child Development, 71, 329–338.

DeLoache, J. S., & Burns, N. M. (1994). Early understanding of the representational function of pictures. Cognition, 52, 83–110.

Demetriou, A. (Ed.). (1988). The neo-Piagetian theories of cognitive development: Toward an integration. Amsterdam: North-Holland.

Demetriou, A. (1998). Cognitive development. In A. Demetriou, W. Doise, & K. F. M. van Lieshout (Eds.), Life-span developmental psychology (pp. 179–269). London: Wiley.

Demetriou, A. (2000). Organization and development of self-understanding and self-regulation: Toward a general theory. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 209–251). San Diego: Academic.

Demetriou, A. (2002). Tracing psychology’s invisible giant and its visible guards. In R. J. Sternberg & E. Grigorenko (Eds.), The general factor of intelligence: Fact or fiction? (pp. 3–19). Mahwah: Lawrence Erlbaum.

Demetriou, A. (2004). Mind, intelligence, and development: Ageneral cognitive, differential, and developmental theory of the mind. In A. Demetriou & A. Raftopoulos (Eds.), Developmental change: Theories, models and measurement (pp. 21–73). Cambridge: Cambridge University Press.

Demetriou, A., & Bakracevic, K. (2009). Cognitive development from adolescence to middle age: From environment-oriented reasoning to social understanding and self-awareness. Learning and Individual Differences, 19, 181–194.

Demetriou, A., & Efklides, A. (1989). The person’s conception of the structures of developing intellect: Early adolescence to middle age. Genetic, Social, and General Psychology Monographs, 115, 371–423.

Demetriou, A., & Efklides, A. (1994). Structure, development, and dynamics of developing mind: A meta- Piagetian theory. In A. Demetriou & A. Efklides (Eds.), Intelligence, mind, and reasoning: Structure and development (pp. 75–110). Amsterdam: North-Holland.

Demetriou, A., & Kazi, S. (2001). Unity and modularity in the mind and the self: Studies on the relationships between self-awareness, personality, and intellectual development from childhood to adolescence. London: Routledge.

Demetriou, A., & Kazi, S. (2006). Self-awareness in g (with processing efficiency and reasoning). Intelligence, 34, 297–317.

Demetriou, A., & Kyriakides, L. (2006). A Rasch-measurement model analysis of cognitive developmental sequences: Validating a comprehensive theory of cognitive development. British Journal of Educational Psychology, 76, 209–242.

Demetriou, A., & Panaoura, R. (2006). Mathematics in the mind: Its place, architecture, and development. In L. Verschaffel, F. Dochy, M. Boekaerts, & S. Vosniadou (Eds.). Instructional psychology: Past, present and future trends. Fifteen essays in honor of Erik De Corte (Advances in Learning and Instruction Series) (pp. 19–38). Amsterdam: Pergamon.

Demetriou, A., & Raftopoulos, A. (1999). Modeling the developing mind: From structure to change. Developmental Review, 19, 319–368.

Demetriou, A., Gustafsson, J.-E., Efklides, A., & Platsidou, M. (1992). Structural systems in developing cognition, science, and education. In A. Demetriou, M. Shayer, & A. Efklides (Eds.), Neo-Piagetian theories of cognitive development: Implications and applications for education (pp. 79–103). London: Routledge.

Demetriou, A., Efklides, A., Platsidou, M. (1993). The architecture and dynamics of developing mind: Experiential structuralism as a frame for unifying cognitive developmental theories. Monographs of the Society for Research in Child Development, 58, (Serial No. 234).

Demetriou, A., Christou, C., Spanoudis, G., Platsidou, M. (2002). The development of mental processing: Efficiency, working memory, and thinking. Monographs of the Society of Research in Child Development, 67, (Serial No. 268).

Demetriou, A., Kyriakides, L., & Avraamidou, C. (2003). The missing link in the relations between intelligence and personality. Journal of Research in Personality, 37, 547–581.

Demetriou, A., Zhang, X. K., Spanoudis, G., Christou, C., Kyriakides, L., & Platsidou, M. (2005). The architecture, dynamics and development of mental processing: Greek, Chinese or universal? Intelligence, 33, 109–141.

Demetriou, A., Mouyi, A., & Spanoudis, G. (2008). Modeling the structure and development of g. Intelligence, 5, 437–454.

Demetriou, A., Mouyi, A., & Spanoudis, G. (2010a). The development of mental processing. In W. F. Overton (Ed.), Biology, cognition and methods across the life-span. Vol. 1: Handbook of life-span development (pp. 306–343). Hoboken: Wiley (Editor-in-chief: R. M. Lerner).

Demetriou, A., Spanoudis, G., & Mouyi, A. (2010b). A three-level model of the developing mind: Functional and neuronal substantiation. In M. Ferrari & L. Vuletic (Eds.), The Developmental relations between mind, brain, and education: Essays in honor of Robbie Case (pp. 9–48). New York: Springer.

Educ Psychol Rev (2011) 23:601–663 657

Detterman, D. K. (2000). General intelligence and the definition of phenotypes. In G. R. Bock, J. A. Goode, & K. Webb (Eds.), The nature of intelligence. Novartis Foundation Symposium 233 (pp. 136–148). Chichester: Wiley.

Dewar, K., & Xu, F. (2010). Induction, overhypothesis, and the origin if abstract knowledge: Evidence from 9-month-old infants. Psychological Science, 21, 1871–1877.

diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22, 293–331.

Dixon, J. A., Stephen, D. G., Boncoddo, R. A., & Anastas, J. (2010). The self-organization of cognitive structure. In B. Ross (Ed.), The psychology of learning & motivation (Vol. 52, pp. 343–384). San Diego: Elsevier.

Duncan, J. (2010). How intelligence happens. New Haven: Yale University Press. Efklides, A., Demetriou, A., & Gustafsson, J.-E. (1992). Training, cognitive change and individual

differences. In A. Demetriou, M. Shayer, & A. Efklides (Eds.), Neo-piagetian theories of cognitive development: Implications and applications for education (pp. 122–143). London: Routledge.

Engel de Abreu, P. M. J., Conway, A. R. A., & Gathercole, S. E. (2010). Working memory and fluid intelligence in young children. Intelligence, 38, 552–561.

English, L. D. (1998). Children’s reasoning in solving relational problems of deduction. Thinking and Reasoning, 4, 249–281.

Ennis, R. H. (1962). A concept of critical thinking. Harvard Educational Review, 32, 81–111. Ennis, R. H. (1987). A taxonomy of critical thinking dispositions and abilities. In J. Baron & R. Sternberg

(Eds.), Teaching thinking skills: Theory and practice (pp. 9–26). New York: W.H. Freeman. Ennis, R. H. (1996). Critical thinking. Upper Saddle River: Prentice-Hall. Ercikan, K. (2006). Developments in assessment of student learning. In P. A. Alexander & P. H. Winne

(Eds.), Handbook of educational psychology (pp. 929–952). Mahwah: Lawrence Erlbaum. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245. Ericsson, K. A., & Lehmann, A. C. (1996). Expert and exceptional performance: Evidence of maximal

adaptation to task constraints. Annual Review of Psychology, 47, 273–305. Finke, R. A. (1993). Mental imagery and creative discovery. In B. Roskos-Ewoldsen, M. J. Intons-Peterson,

& R. E. Anderson (Eds.), Imagery, creativity, and discovery: A cognitive perspective (pp. 255–286). Amsterdam: Elsevier.

Fireman, G. (1996). Developing a plan for solving a problem: A representational shift. Cognitive Development, 11, 107–122.

Fischer, K. W. (1980). A theory of cognitive development: The control and construction of hierarchies of skills. Psychological Review, 87, 477–531.

Fisher, A. V. (2010). Mechanisms of induction early in development. In M. Banich & D. Caccamise (Eds.), Generalization of knowledge: Multidisciplinary perspectives (pp. 89–112). New York: Psychology Press.

Flavell, J. H., Green, F. L., Flavell, E. R. (1995).Young children’s knowledge about thinking. Monographs of the Society for Research in Child Development, 60 (1, Serial No. 243).

Flavell, J. H., Green, F. L., Flavell, E. R., & Grossman, J. B. (1997). The development of children’s knowledge about inner speech. Child Development, 68, 39–47.

Flook, L., Smalley, S. L., Kitil, M. J., Galla, B. M., Kaiser-Greenland, S., Locke, et al. (2010). Effects of mindfulness awareness practices on executive functions in elementary school children. Journal of Applied School Psychology, 26, 70–95.

Flynn, J. R. (2009). What is intelligence: Beyond the Flynn effect. Cambridge: Cambridge University Press. Fodor, J. A. (1976). The language of thought. Hassocks: Harvester Press. Furedy, C., & Furedy, J. (1985). Critical thinking: Towards research and dialogue. In Donald & Sullivan

(Eds.), Using research to improve teaching (New Directions for Teaching and Learning No. 23). San Francisco: Jossey-Bass.

Gardner, H. (1983). Frames of mind. The theory of multiple intelligences. New York: Basic Books. Gauffroy, C., & Barrouillet, P. (2011). The primacy of thinking about possibilities in the development of

reasoning. Developmental Psychology, 47, 1000–1011. Gelman, S. A. (2003). The essential child. Oxford: Oxford University Press. Gelman, R., & Brenneman, K. (1994). First principles can support both universal and culture-specific

learning about number and music. In L. Hirschfeld & S. Gelman (Eds.), Mapping the mind: Domains, culture and cognition (pp. 369–390). Cambridge: Cambridge University Press.

Gelman, S. A., & Coley, J. D. (1990). The importance of knowing a dodo is a bird: Categories and inferences in 2-year-old children. Developmental Psychology, 26, 796–804.

Gentner, D. (1989). The mechanisms of analogical learning. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 199–241). Cambridge: Cambridge University Press.

658 Educ Psychol Rev (2011) 23:601–663

Gentner, D., & Rattermann, M. J. (1991). Language and the career of similarity. In S. A. Gelman & J. P. Byrnes (Eds.), Perspectives on language and thought: Interrelations in development (pp. 225–277). Cambridge: Cambridge University Press.

Gibson, E. J., & Pick, A. D. (2003). An ecological approach to perceptual learning and development. Oxford: Oxford University Press.

Glassner, A., & Schwarz, B. B. (2007). What stands and develops between creative and critical thinking? Argumentation? Thinking Skills and Creativity, 2, 10–18.

Gobet, F. (1998). Expert memory: A comparison of four theories. Cognition, 66, 115–152. Grigorenko, E. L. (2002). Other than g. The value of persistence. In R. J. Sternberg & E. L. Grigorenko (Eds.),

The general factor of intelligence: How general is it? (pp. 299–327). Mahwah: Lawrence Erlbaum. Gustafsson, J.-E. (1984). A unifying model for the structure of intellectual abilities. Intelligence, 8, 179–203. Gustafsson, J.-E. (2008). Schooling and intelligence: Effects of track of study on level and profile of

cognitive abilities. In P. C. Kyllonen, R. D. Roberts, & L. Stankov (Eds.), Extending intelligence: Enhancement and new constructs (pp. 37–59). New York: Lawrence Erlbaum.

Gustafsson, J. E., & Undheim, J. O. (1996). Individual differences in cognitive functions. In D. C. Berliner & R. C. Calfee (Eds.), Handbook of educational psychology (pp. 186–242). New York: Macmillan.

Halford, G. S. (1993). Children’s understanding: The development of mental models. Hillsdale: Lawrence Erlbaum. Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity:

Implications for comparative, developmental, and cognitive psychology. The Behavioral and Brain Sciences, 21, 803–864.

Handley, S. J., Capon, A., Beveridge, M., Dennis, I., Evans, J. St. B. T. (2004). Working memory, inhibitory control and the development of children’s reasoning. Thinking and Reasoning, 10, 175–195.

Harris, P., & Nunez, M. (1996). Understanding of permission rules by preschool children. Child Development, 67, 1572–1591.

Harter, S. (1999). The construction of the self: A developmental perspective. New York: The Guilford Press. Haun, D. B. M., Jordan, F. M., Vallortigara, G., & Clayton, N. S. (2010). Origins of spatial, temporal and

numerical cognition: Insights from comparative psychology. Trends in Cognitive Sciences, 14, 552–560. Heit, E., & Rotello, C. M. (2010). Relations between inductive reasoning and deductive reasoning. Journal

of Experimental Psychology. Learning, Memory, and Cognition, 36, 805–812. Hirschfeld, L. A., & Gelman, S. A. (Eds.). (1994). Mapping the mind: Domain specificity in cognition and

culture. New York: Cambridge University Press. Hmelo-Silver, C. E., & Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex

system from the perspective of structures, behaviors, and functions. Cognitive Science, 28, 127–138. Holland, J., Holyoak, K., Nisbett, R., & Thagard, P. (1986). Induction: Processes of inference, learning, and

discovery. Cambridge: MIT Press. Hornung, C., Brunner, M., Reuter, R. A. P., & Martin, R. (2011). Children’s working memory: Its structure

and relationship to fluid intelligence. Intelligence, 39, 210–221. Hunt, E. B. (2002). Thoughts on thought. Mahwah: Lawrence Erlbaum. Hunt, E. B. (2011). Human intelligence. New York: Cambridge University Press. Huttenlocher, J., Vasilyeva, M., Newcombe, N., & Duffy, S. (2008). Developing symbolic capacity one step

at a time. Cognition, 106, 1–12. Jacobs, J. E., & Klaczynski, P. A. (2002). The development of judgment and decision making during

childhood and adolescence. Current Directions in Psychological Science, 11, 145–149. Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Perrig, W. J. (2008). Improving fluid intelligence with training

on working memory. Proceedings of the National Academy of Sciences, 105, 6829–6833. Jensen, A. R. (1998). The g factor: The science of mental ability. Westport: Praeger. Johansen, M. K., & Palmeri, T. J. (2002). Are there representational shifts during category learning?

Cognitive Psychology, 45, 482–553. Johnson, W., te Nijenhuis, J., & Bouchard, T. J. (2008). Still just 1 g: Consistent results from five test

batteries. Intelligence, 36, 81–95. Johnson-Laird, P. N. (2001). Mental models and deduction. Trends in Cognitive Sciences, 5, 434–442. Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and

Development, 48, 63–85. Jonassen, D. H. (2003). Learning to solve problems: An instructional design guide. New York: Pfeifer. Jones, L., & Stuth, G. (1997). The uses of mental imagery in athletics: An overview. Applied and Preventive

Psychology, 6, 101–115. Kagan, J. (2009). The three cultures: Natural sciences, social sciences, and the humanities in the 21st

century. Cambridge: Cambridge University Press. Kail, R. (1991). Developmental change in speed of processing during childhood and adolescence.

Psychological Bulletin, 109, 490–501.

Educ Psychol Rev (2011) 23:601–663 659

Kail, R. (1993). Processing time decreases globally at an exponential rate during childhood and adolescence. Journal of Experimental Child Psychology, 56, 254–265.

Kalyuga, S., & Hanham, J. (2011). Instructing in generalized knowledge structures to develop flexible problem solving skills. Computers in Human Behavior, 27, 63–68.

Kalyuga, S., Renkl, A., & Paas, F. (2010). Facilitating flexible problem solving: A cognitive load perspective. Educational Psychology Review, 22, 175–186.

Kargopoulos, P., & Demetriou, A. (1998). Logical and psychological partitioning of mind: Depicting the same picture? New Ideas in Psychology, 16, 61–88.

Karmiloff-Smith, A. (1992). Beyond modularity: A developmental perspective on cognitive science. Cambridge: MIT Press.

Keen, R. (2011). The development of problem solving in young children: A critical cognitive skill. Annual Review of Psychology, 62, 1–21.

Keil, F. C. (2011). Science starts early. Science, 331, 1022–1023. Keogh, B. K. (2003). Temperament in the classroom: Understanding individual differences. Baltimore:

Brookes Publishing. King, P. M., & Kitchener, K. S. (1994). Developing reflective judgment. New York. Jossey-Bass. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance instruction does not work: An

analysis of the failure of constructivist, discovery, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41, 75–86.

Klaczynski, P. A., & Daniel, D. B. (2005). Individual differences in conditional reasoning: A dual-process account. Thinking and Reasoning, 11, 305–325.

Klauer, K. C., Meiser, T., & Naumer, B. (2000). Training propositional reasoning. The Quarterly Journal of Experimental Psychology, 53, 868–895.

Klingberg, T., Forssberg, H., & Westerberg, H. (2002). Training of working memory in children with ADHD. Journal of Clinical and Experimental Neuropsycholy, 24(6), 781–791.

Kopp, C. B. (2011). Development in the early years: Socialization, motor development, and consciousness. Annual Review of Psychology, 62, 165–187.

Krumm, S., Ziegler, M., & Buehner, M. (2008). Reasoning and working memory as predictors of school grades. Learning and Individual Differences, 18, 248–257.

Kuhn, D. (1991). The skills of argument. New York: Cambridge University Press. Kuhn, D. (2009). Do students need to be taught how to reason? Educational Research Review, 4, 1–6. Kuhn, D., & Pease, M. (2006). Do children and adults learn differently? Journal of Cognition and

Development, 7, 279–293. Kyllonen, P., & Christal, R. E. (1990). Reasoning ability is (little more than) working-memory capacity?

Intelligence, 14, 389–433. Kyriakides, L., & Luyten, H. (2009). The contribution of schooling to the cognitive development of

secondary education students in Cyprus: An application of regression discontinuity with multiple cut-off points. School Effectiveness and School Improvement, 20, 167–186.

Labouvie-Vief, G. (2005). Self-with-other representations and the organization of the self. Journal of Research in Personality, 39, 185–205.

Leslie, A., Friedman, O., & German, T. P. (2004). Core mechanisms in “theory of mind”. Trends in Cognitive Sciences, 12, 528–533.

Linn, M. C., & Eylon, B.-S. (2011). Science learning and instruction: Taking advantage of technology to promote knowledge integration. London: Routledge.

Lynn, R., & Vanhanen, T. (2006). IQ and global inequality. Augusta: Washington Summit Publishers. Mandler, J. M. (2004). The foundations of mind. Oxford: Oxford University Press. Markovits, H., & Barrouillet, P. (2002). The development of conditional reasoning: A mental model account.

Developmental Review, 22, 5–36. McBride-Chang, C., Zhou, Y., Cho, J.-R., Aram, D., Levin, I., & Tolchinsky, L. (2011). Visual spatial

skill: A consequence of learning to read? Journal of Experimental Child Psychology, 109, 256– 262.

McCune, L. (2008). How children learn to learn language. New York: Oxford University Press. Meltzoff, A. N., Kuhl, P. K., Movellan, J., & Sejnowski, T. J. (2009). Foundations for a new science of

learning. Science, 325, 284–288. Mercier, H., & Sperber, D. (2011). Why do humans reason? Arguments for an argumentative theory.

Behavioral and Brain Sciences (in press). Miller, L. T., & Vernon, P. A. (1992). The general factor in short-term memory, intelligence, and reaction time.

Intelligence, 16, 5–29. Miller, S. A., Custer, W. L., & Nassau, G. (2000). Children’s understanding of the necessity of logically

necessary truths. Cognitive Development, 15, 383–403.

660 Educ Psychol Rev (2011) 23:601–663

Molenbergs, P., Cunnigton, R., & Mattingley, J. B. (2009). Is the mirror system involved in imitationV A short review and meta-analysis. Neuroscience and Behavioral Reviews, 33, 975–980.

Moshman, D. (1990). The development of metalogical understanding. In W. F. Overton (Ed.), Reasoning, necessity, and logic: Developmental perspectives (pp. 205–225). Hillsdale: Lawrence Erlbaum.

Moshman, D. (1994). Reasoning, metareasoning and the promotion of rationality. In A. Demetriou & A. Efklides (Eds.), Mind, intelligence, and reasoning: Structure and development (pp. 135–150). Amsterdam: Elsevier.

Moshman, D. (2011). Adolescent rationality and development: Cognition, morality, and identity (3rd ed.). New York: Psychology Press.

Mouyi, A. (2008). Developmental dynamics binding processing efficiency, working memory, and reasoning: A longitudinal study. Unpublished doctoral dissertation. Nicosia: University of Cyprus.

Nesher, P., & Sukenik, M. (1991). The effect of formal representation on the learning of ratio concepts. Learning and Instruction, 1, 161–175.

Nisbett, R. E. (2009). Intelligence and how to get it: Why schools and cultures count. New York: Norton. Olson, D. (2003). Psychological theory and educational reform: How school remakes mind and society.

Cambridge: Cambridge University Press. Overton, W. F. (2010). Life-span development: Concepts and issues. In W. F. Overton (Ed.), Biology,

cognition and methods across the life-span. Vol. 1: Handbook of life-span development (pp. 1–29). Hoboken: Wiley (Editor-in-chief: Lerner RM).

Overton, W. F., Byrnes, J. P., & O’ Brien, D. P. (1985). Developmental and individual differences in conditional reasoning: The role of contradiction training and cognitive style. Developmental Psychology, 21, 692–701.

Panaoura, A., Gagatsis, A., & Demetriou, A. (2009). An intervention to the mathematical performance: Self- regulation in mathematics and mathematical modeling. Acta Didactica Universitatis Comenianae, 9, 63–79.

Pascal-Leone, J. (1988). Organismic processes for neo-Piagetian theories: A dialectical causal account of cognitive development. In A. Demetriou (Ed.), The neo-Piagetian theories of cognitive development: Toward an integration (pp. 25–64). Amsterdam: North-Holland.

Pascarella, E. T., & Terenzin, P. T. (2005). How college affects students. San Francisco: Jossey-Bass. Pascual-Leone, J. (1970). A mathematical model for the transition rule in Piaget’s developmental stages. Acta

Psychologica, 63, 301–345. Piaget, J. (1970). Piaget’s theory. In P. H. Mussen (Ed.), Carmichael’s handbook of child development (pp.

703–732). New York: Wiley. Piaget, J. (1980). Concerning creativity: Three methods. In J.-C. Bringuier (Ed.), Conversations with Jean

Piaget (pp. 126–132). Chicago: University of Chicago Press (Original work published in 1977). Piaget, J. (2001). Studies in reflecting abstraction. London: Psychology Press. Piazza, M. (2010). Neurocognitive start-up tools for symbolic number representations. Trends in Cognitive

Sciences, 14, 542–551. Pind, J., Gunnarsdottir, E. K., & Johannesson, H. S. (2003). Raven’s standard progressive matrices: New

school age norms and a study of the test’s validity. Personality and Individual Differences, 34, 375–386. Posner, M. I., & Rothbart, M. K. (2006). Educating the human brain. New York: American Psychological

Association. Printz, W. (2003). Emerging selves: Representational foundations of subjectivity. Consciousness and

Cognition, 12, 515–528. Raffray, C. N., & Pickering, M. J. (2010). How do people construct logical form during language

comprehension? Psychological Science, 21(8), 1090–1097. Raftopoulos, A., & Constantinou, C. P. (2004). Types of cognitive change: A dynamical, connectionist

account. In A. Demetriou & A. Raftopoulos (Eds.), Developmental change: Theories, models and measurement (pp. 74–117). Cambridge: Cambridge University Press.

Rasmussen, C., & Bisanz, J. (2005). Representation and working memory in early arithmetic. Journal of Experimental Child Psychology, 91, 137–157.

Reed, S. K. (1993). Imagery and discovery. In B. Roskos-Ewoldsen, M. J. Intons-Peterson, & R. E. Anderson (Eds.), Imagery, creativity, and discovery: A cognitive perspective (pp. 287–328). Amsterdam: Elsevier.

Reynolds, C. A., Finkel, D., McArdle, J. J., Gatz, M., Berg, S., & Pedersen, N. L. (2005). Quantitative genetic analysis of latent growth curve models of cognitive abilities in adulthood. Developmental Psychology, 41, 3–16.

Ricco, R. B. (2010). The development of deductive reasoning across the lifespan. In W. F. Overton (Ed.), Biology, cognition, and methods across the life-span. Vol.1: Handbook of life-span development (pp. 391–430). Hoboken: Wiley (Editor-in-chief: R. M. Lerner).

Ricco, R. B., & Overton, W. F. (2011). Dual systems Competence - Procedural processing: A relational developmental systems approach to reasoning. Developmental Review doi:10.1016/j.dr.2011.07.005.

Educ Psychol Rev (2011) 23:601–663 661

Riegel, K. F. (1973). Dialectic operations: The final period of cognitive development. Human Development, 16, 346–370.

Rindermann, H., & Neubauer, A. C. (2004). Processing speed, intelligence, creativity, and school performance: Testing of causal hypotheses using structural equation models. Intelligence, 32, 573–589.

Rips, L. J. (1994). The psychology of proof. Deductive reasoning in human thinking. Cambridge: MIT Press. Rizzolatti, G., & Craighero, L. (2004). The mirror-neuron system. Annual Review of Neuroscience, 27, 169–192. Rohde, T. E., & Thompson, L. A. (2007). Predicting academic achievement with cognitive ability.

Intelligence, 35, 83–92. Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology:

General, 104, 192–322. Runco, M. A., & Pritzker, S. R. (Eds.). (1999). Encyclopedia of creativity. San Diego: Academic. Sackett, P. R., Schmitt, N., Ellingson, J. E., & Kabin, M. B. (2001). High-stakes testing in employment,

credentialing, and higher education: Prospects in a post-affirmative-action world. American Psychologist, 56, 302–318.

Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 35–54. Schaie, K. W. (1996). Adulthood and old age. In T. Husen & T. N. Postlewaithe (Eds.), International

encyclopedia of education (pp. 163–168). Oxford: Pergamon. Shayer, M., & Adey, P. (2002). Learning intelligence: Cognitive acceleration across the curriculum from 5 to

15 years. Milton Keynes: Open University Press. Shayer, M., & Adhami, M. (2003). Realising the cognitive potential of children 5–7 with a mathematics

focus. International Journal of Educational Research, 39, 743–775. Shayer, M., & Wylam, H. (1978). The distribution of Piagetian stages of thinking in British middle and

secondary school children II: 14–16 year-olds and sex differentials. British Journal of Educational Psychology, 948, 62–70.

Shayer, M., Küchemann, D. E., & Wylam, H. (1976). The distribution of Piagetian stages of thinking in British middle and secondary school children. British Journal of Educational Psychology, 46, 164–173.

Shayer, M., Demetriou, A., & Pervez, M. (1988). The structure and scaling of concrete operational thought: Three studies in four countries. Genetic, Social, and General Psychology Monographs, 114, 307–376.

Sheppard, L. D., & Vernon, P. A. (2008). Intelligence and speed of information-processing: A review of 50 years of research. Personality and Individual Differences, 44, 535–551.

Siegel, H. (1988). Educating reason: Rationality, critical thinking, and education. London: Routledge. Siegel, H. (1989a). Epistemology, critical thinking, and critical thinking pedagogy. Argumentation, 3, 127–140. Siegel, H. (1989b). The rationality of science, critical thinking, and science education. Synthese, 80, 9–41. Siegler, R. S. (1996). Emerging minds: The process of change in children’s thinking. Oxford: Oxford

University Press. Simonton, D. K. (2000). Creative development as acquired expertise: Theoretical issues and an empirical

test. Developmental Review, 20, 283–318. Sloutsky, V. M., & Fisher, A. V. (2011). The development of categorization. Psychology of Learning and

Motivation, 54, 141–166. Smith, L. (2009). Piaget’s pedagogy. In U. Muller, J. I. M. Carpendale, & L. Smith (Eds.), The Cambridge

companion to Piaget (pp. 324–343). Cambridge: Cambridge University Press. Snow, R. (1989). Aptitude–treatment interaction as a framework on individual differences in learning. In P.

Ackermann, R. J. Sternberg, & R. Glaser (Eds.), Learning and individual differences (pp. 13–59). New York: W.H. Freeman.

Spearman, C. (1904). “General intelligence” objectively determined and measured. The American Journal of Psychology, 15, 201–293.

Stanovich, K. E. (2009). What intelligence tests miss: The psychology of rational thought. New Haven: Yale University Press.

Stearns, P. N. (2006). Childhood in world history. London: Routledge. Stein, M., & Burchartz, B. (2009). The Invisible wall project: Reasoning and problem solving processes of

primary and lower secondary students. Mathematical Thinking and Learning, 8, 65–90. Sternberg, R. J. (Ed.). (1999). Handbook of creativity. New York: Cambridge University Press. Sternberg, R. J. (2005). Creativity or creativities? International Journal of Human Computer Studies, 63,

370–382. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and

Instruction, 4, 295–312. Téglás, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J. B., & Bonatti, L. (2011). Pure reasoning in 12-

month-old infants as probabilistic inference. Science, 332, 1054–1059. Thorell, L. B., Lindqvist, S., Bergman Nutley, S., Bohlin, G., & Klingberg, T. (2009). Training and transfer

effects of executive functions in preschool children. Developmental Science, 12, 106–113.

662 Educ Psychol Rev (2011) 23:601–663

Tillman, C. M., Nyberg, L., & Bohlin, G. (2008). Working memory components and intelligence in children. Intelligence, 36, 394–402.

Tsamir, P., Tirosh, D., Tabach, M., & Levenson, E. (2010). Multiple solution methods and multiple outcomes—Is it a task for kindergarten children? Educational Studies in Mathematics, 73, 217–231.

Tyrrel, D. J., Stauffer, L. B., & Snowman, L. G. (1991). Perception of abstract identity/difference relationships by infants. Infant Behavior & Development, 14, 125–129.

Unsworth, N., & Spillers, G. J. (2010). Working memory capacity: Attention control, secondary memory, or both? A direct test of the dual-component model. Journal of Memory and Language, 62, 392–406.

van der Maas, H. L. J., Dolan, C. V., Grasman, R. P. P. P., Wicherts, G. J. M., Huizenga, H. M., & Raijmakers, M. E. J. (2006). A dynamical model of general intelligence: The positive manifold of intelligence by mutualism. Psychological Review, 113, 842–861.

van Fraassen, B. C. (2010). Scientific representation: Paradoxes of perspective. Oxford: Oxford University Press.

van Geert, P. (1994). Dynamic systems of development: Change between complexity and chaos. New York: Harvester Wheatsheaf.

Vosniadou, S. (Ed.). (2008). International handbook of research on conceptual change. London: Routledge. Vosniadou, S., & Brewer, W. F. (1992). Mental models of the earth: A study of conceptual change in

childhood. Cognitive Psychology, 24, 535–585. Wagner, S., Winner, E., Cicchetti, D., & Gardner, H. (1981). "Metaphorical" mapping in human infants. Child

Development, 52, 728–731. Watson, G. B., & Glaser, E. M. (1980). WGCTA Watson-Glaser Critical Thinking Appraisal Manual: Forms

A and B. San Antonio: The Psychological Corporation. Wellman, H. M. (1990). The child’s theory of mind. Cambridge: Bradford. Westerberg, H. (2004). Working memory: Development, disorders, and training. Stockholm: Karolinska

Institutet. Whitherington, D. C. (2011). Taking emergence seriously: The centrality of circular causality for dynamic

systems approaches to development. Human Development, 54, 66–92. Wildenger, L. K., Hofer, B. K., & Burr, J. E. (2010). Epistemological development in very young knowers. In

L. Bendixen & F. Haerle (Eds.), Personal epistemology in the classroom: Theory, research, and implications for practice (pp. 220–257). Cambridge: Cambridge University Press.

Williamson, R. A., Jaswal, V. K., & Meltzoff, A. N. (2010). Learning the rules: Observation and imitation of a sorting strategy by 36-month-old children. Developmental Psychology, 46, 57–65.

Winship, C., & Korenman, S. (1997). Does staying in school make you smarter? The effect of education on IQ in the Bell Curve. In B. Devlin, S. E. Fienberg, D. P. Resnick, & K. Roeder (Eds.), Intelligence, genes, and success. Scientists respond to the Bell Curve (pp. 215–234). New York: Springer.

Wood, J. N. (2011). A core knowledge architecture of visual working memory. Journal of Experimental Psychology. Human Perception and Performance, 37, 357–381.

Xu, F., & Garcia, V. (2008). Intuitive statistics by 8-month-old infants. Proceeding of the National Academy of Sciences, 105, 5012–5015.

Yamagata, K. (2007). Differential emergence of representational systems: Drawings, letters, and numerals. Cognitive Development, 22, 244–257.

Yerushalmy, M. (1997). Mathematizing verbal descriptions of situations: A language to support modeling. Cognition and Instruction, 15, 207–264.

Zelazo, P. D. (2004). The development of conscious control in childhood. Trends in Cognitive Sciences, 8, 12–17.

Zelazo, P. D. (2011). Mindfulness training in childhood. Human Development, 54, 61–65. Zelazo, P. D., & Frye, D. (1998). Cognitive complexity and control: The development of executive function.

Current Directions in Psychological Science, 7, 121–126. Zelazo, P. D., Müller, U., Frye, D., Marcovitch, S., Argitis, G., Boseovski, J., Chiang, J. K., Hongwanishkul,

D., Schuster, B. V., Sutherland, S., Carlson, S. M. (2003). The development of executive function in early childhood. Monographs of the Society for Research in Child Development, 68, (Serial No. 3).

Zelazo, P. D., Qu, L., & Müller, U. (2005). Hot and cool aspects of executive function: Relations with early development. In W. Schneider, R. Schumann, R. Hengsteler, & B. Sodian (Eds.), Young children’s cognitive development: Interrelationships among executive functioning, working memory, verbal ability, and theory of mind (pp. 71–93). Mahwah: Lawrence Erlbaum.

Zosh, J. M., Halberda, J., & Feigenson, L. (2011). Memeory for multiple visual ensembles in infancy. Journal of Experimental Psychology. General, 140, 141–158.

Educ Psychol Rev (2011) 23:601–663 663

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