Pub Date : 2025-11-30DOI: 10.1016/j.lindif.2025.102844
Jiesi Guo , Samuel Greiff , Xin Tang
{"title":"Shaping the socio-emotional landscape: Advances, mechanisms, and contexts in learning and individual differences","authors":"Jiesi Guo , Samuel Greiff , Xin Tang","doi":"10.1016/j.lindif.2025.102844","DOIUrl":"10.1016/j.lindif.2025.102844","url":null,"abstract":"","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102844"},"PeriodicalIF":9.0,"publicationDate":"2025-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.lindif.2025.102835
Dana Miller-Cotto , James P. Byrnes
Identifying malleable predictors of academic achievement is critical for supporting individual differences in learning outcomes and informing targeted interventions. However, practical constraints often require reducing the number of predictors while still accounting for meaningful variance. In this study, we combined two machine learning approaches (ridge regression and lasso regression) with a person-centered technique, latent profile transition analysis (LPTA), to isolate key cognitive and motivational factors that differentiate learners and predict academic growth. Using a large, nationally representative longitudinal dataset, machine learning analyses identified three robust predictors from 14 propensity variables: prior reading skills, motivation, and working memory. Subsequent LPTA revealed five distinct profiles of learners based on different combinations of these variables, with most children remaining in stable profiles across kindergarten and first grade, though some showed upward transitions. Importantly, these profiles transcended socioeconomic status and diagnostic categories, and they significantly predicted growth in mathematics achievement, a skill not used to create the profiles. Findings highlight meaningful and stable individual differences in cognitive and motivational profiles that shape learning trajectories, with implications for theory development, early identification, and the development of tailored intervention strategies.
{"title":"Identifying individual cognitive and motivational profiles predictive of academic growth: A combined machine learning and person-centered approach","authors":"Dana Miller-Cotto , James P. Byrnes","doi":"10.1016/j.lindif.2025.102835","DOIUrl":"10.1016/j.lindif.2025.102835","url":null,"abstract":"<div><div>Identifying malleable predictors of academic achievement is critical for supporting individual differences in learning outcomes and informing targeted interventions. However, practical constraints often require reducing the number of predictors while still accounting for meaningful variance. In this study, we combined two machine learning approaches (ridge regression and lasso regression) with a person-centered technique, latent profile transition analysis (LPTA), to isolate key cognitive and motivational factors that differentiate learners and predict academic growth. Using a large, nationally representative longitudinal dataset, machine learning analyses identified three robust predictors from 14 propensity variables: prior reading skills, motivation, and working memory. Subsequent LPTA revealed five distinct profiles of learners based on different combinations of these variables, with most children remaining in stable profiles across kindergarten and first grade, though some showed upward transitions. Importantly, these profiles transcended socioeconomic status and diagnostic categories, and they significantly predicted growth in mathematics achievement, a skill not used to create the profiles. Findings highlight meaningful and stable individual differences in cognitive and motivational profiles that shape learning trajectories, with implications for theory development, early identification, and the development of tailored intervention strategies.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102835"},"PeriodicalIF":9.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1016/j.lindif.2025.102843
Sirui Ren, Jeffrey A. Greene, Matthew L. Bernacki, Leiming Ding
Why do some self-regulated learning (SRL) interventions seem to benefit less competent students more than their competent peers (i.e., compensatory effect), but others seem to benefit only the already competent students (i.e., Matthew effects)? We propose the Resource-Intervention Match (RIM) framework to explain these differential outcomes. Intervention effects depend on the (mis-)match between learners' existing SRL resources and specific intervention features. We conceptualize SRL resources as comprising three components: metacognitive knowledge, metacognitive skills, and motivational-affective resources. When learners' resources align with intervention demands, learners experience gains in performance; misalignment creates non-productive experiences that hinder progress. A critical but overlooked factor is metacognitive experiences (e.g., feelings of difficulty, confidence, and satisfaction) that emerge during learning. These experiences serve as the mediating mechanism through which resource-intervention (mis-)matches influence intervention outcomes. The RIM framework provides researchers and practitioners with a systematic approach to diagnosing, predicting, and optimizing SRL intervention effects across individual differences.
Educational relevance and implications statement
This research explains why some learning interventions help struggling students catch up (compensatory effects) whereas others primarily benefit already-successful students (Matthew effects). We found that effectiveness depends on matching support to specific gaps in students' self-regulated learning: their knowledge about effective strategies, their ability to actually use these strategies, and their motivation to persist through challenges. Teachers can assess these three components separately through questionnaires and classroom observation, then provide personalized support that adjusts based on each student's needs and gradually fades as they develop skills. This approach transforms students from those requiring constant external guidance into independent learners who can systematically figure out which study approaches work best for their individual needs.
{"title":"Beyond the black box: The resource-intervention match framework for explaining differential effects of self-regulated learning interventions","authors":"Sirui Ren, Jeffrey A. Greene, Matthew L. Bernacki, Leiming Ding","doi":"10.1016/j.lindif.2025.102843","DOIUrl":"10.1016/j.lindif.2025.102843","url":null,"abstract":"<div><div>Why do some self-regulated learning (SRL) interventions seem to benefit less competent students more than their competent peers (i.e., compensatory effect), but others seem to benefit only the already competent students (i.e., Matthew effects)? We propose the Resource-Intervention Match (RIM) framework to explain these differential outcomes. Intervention effects depend on the (mis-)match between learners' existing SRL resources and specific intervention features. We conceptualize SRL resources as comprising three components: <em>metacognitive knowledge</em>, <em>metacognitive skills</em>, and <em>motivational-affective resources</em>. When learners' resources align with intervention demands, learners experience gains in performance; misalignment creates non-productive experiences that hinder progress. A critical but overlooked factor is <em>metacognitive experiences</em> (e.g., feelings of difficulty, confidence, and satisfaction) that emerge during learning. These experiences serve as the mediating mechanism through which resource-intervention (mis-)matches influence intervention outcomes. The RIM framework provides researchers and practitioners with a systematic approach to diagnosing, predicting, and optimizing SRL intervention effects across individual differences.</div></div><div><h3>Educational relevance and implications statement</h3><div>This research explains why some learning interventions help struggling students catch up (compensatory effects) whereas others primarily benefit already-successful students (Matthew effects). We found that effectiveness depends on matching support to specific gaps in students' self-regulated learning: their knowledge about effective strategies, their ability to actually use these strategies, and their motivation to persist through challenges. Teachers can assess these three components separately through questionnaires and classroom observation, then provide personalized support that adjusts based on each student's needs and gradually fades as they develop skills. This approach transforms students from those requiring constant external guidance into independent learners who can systematically figure out which study approaches work best for their individual needs.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102843"},"PeriodicalIF":9.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1016/j.lindif.2025.102841
Garvin Brod
Agency has become a central theme in debates on learning with artificial intelligence (AI). Current discussions often reduce agency to the question of who makes the choices: the learner or the AI. This framing, however, is too narrow. Conceptual insights from different disciplines, together with evidence from psychology, indicate that providing learners with the opportunity to make decisions is not enough to claim that they have agency over their learning. Rather, agency requires at least three steps: 1) the opportunity to make decisions, 2) the capacity to make decisions, and 3) the capacity to enact those decisions. The capacity to make and enact decisions develops across childhood and adolescence, leading to substantial individual differences in learners' ability to exercise agency. The three-step approach can sharpen theoretical discussions by distinguishing choice from agency and offer concrete targets for educational interventions aimed at preserving and promoting agency in the age of AI.
{"title":"Agency does not equal choice – conceptualizing agency for learning in the age of AI","authors":"Garvin Brod","doi":"10.1016/j.lindif.2025.102841","DOIUrl":"10.1016/j.lindif.2025.102841","url":null,"abstract":"<div><div>Agency has become a central theme in debates on learning with artificial intelligence (AI). Current discussions often reduce agency to the question of who makes the choices: the learner or the AI. This framing, however, is too narrow. Conceptual insights from different disciplines, together with evidence from psychology, indicate that providing learners with the opportunity to make decisions is not enough to claim that they have agency over their learning. Rather, agency requires at least three steps: 1) the opportunity to make decisions, 2) the capacity to make decisions, and 3) the capacity to enact those decisions. The capacity to make and enact decisions develops across childhood and adolescence, leading to substantial individual differences in learners' ability to exercise agency. The three-step approach can sharpen theoretical discussions by distinguishing choice from agency and offer concrete targets for educational interventions aimed at preserving and promoting agency in the age of AI.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102841"},"PeriodicalIF":9.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-24DOI: 10.1016/j.lindif.2025.102822
R.C. Perry , E. Booth , M.S.C. Thomas , A. Tolmie , M. Röösli , M.B. Toledano , C. Shen , I. Dumontheil
Few studies have isolated associations between socioeconomic status (SES) and executive function (EF) in adolescence, when EF inequalities may be particularly consequential for academic attainment. Using data from the Study of Cognition, Adolescents and Mobile Phones (n = 2726) and multiple regressions, we evaluated relationships between SES indices (parental education and occupation, area-level deprivation, and household poverty) and EF tasks, controlling for demographic factors. Replicating findings from childhood, latent SES and EF measures associated cross-sectionally at age 12 (β = 0.11, [0.07, 0.15]). We further observed a small increase in the socioeconomic EF gradient between 12 and 14 years (β = 0.07, [0.04, 0.11]), with which was specifically associated with parental occupation and household poverty. Working memory span tasks were particularly sensitive to SES. Our results highlight specific SES-EF associations during adolescence and could help identify pupils at risk for cognitive, and therefore academic, challenges who may benefit from targeted support.
Educational relevance and implications
Individual differences in EF skills associate with educational outcomes across development, as well as health and occupational outcomes in adulthood. This study demonstrates that, in a UK sample, SES not only associates with individual differences in EF in childhood, but that over a period as short as two years, parental occupation and household poverty (but not parental education or area deprivation), associate with small but significant increasing differences in adolescents' working memory skills. By isolating specific associations between aspects of SES and EF inequalities, this study suggests family level factors have an enduring influence on cognitive skills into adolescence, which may contribute to the trend of increasing attainment inequalities seen in this age group. The findings help to narrow the pool of likely causal explanations for social inequalities in EF skills and may help to identify pupils who are at risk for cognitive, and therefore academic, challenges.
{"title":"Longitudinal associations between socioeconomic status and executive function during adolescence: Evidence from the SCAMP study","authors":"R.C. Perry , E. Booth , M.S.C. Thomas , A. Tolmie , M. Röösli , M.B. Toledano , C. Shen , I. Dumontheil","doi":"10.1016/j.lindif.2025.102822","DOIUrl":"10.1016/j.lindif.2025.102822","url":null,"abstract":"<div><div>Few studies have isolated associations between socioeconomic status (SES) and executive function (EF) in adolescence, when EF inequalities may be particularly consequential for academic attainment. Using data from the Study of Cognition, Adolescents and Mobile Phones (<em>n</em> = 2726) and multiple regressions, we evaluated relationships between SES indices (parental education and occupation, area-level deprivation, and household poverty) and EF tasks, controlling for demographic factors. Replicating findings from childhood, latent SES and EF measures associated cross-sectionally at age 12 (β = 0.11, [0.07, 0.15]). We further observed a small increase in the socioeconomic EF gradient between 12 and 14 years (β = 0.07, [0.04, 0.11]), with which was specifically associated with parental occupation and household poverty. Working memory span tasks were particularly sensitive to SES. Our results highlight specific SES-EF associations during adolescence and could help identify pupils at risk for cognitive, and therefore academic, challenges who may benefit from targeted support.</div></div><div><h3>Educational relevance and implications</h3><div>Individual differences in EF skills associate with educational outcomes across development, as well as health and occupational outcomes in adulthood. This study demonstrates that, in a UK sample, SES not only associates with individual differences in EF in childhood, but that over a period as short as two years, parental occupation and household poverty (but not parental education or area deprivation), associate with small but significant increasing differences in adolescents' working memory skills. By isolating specific associations between aspects of SES and EF inequalities, this study suggests family level factors have an enduring influence on cognitive skills into adolescence, which may contribute to the trend of increasing attainment inequalities seen in this age group. The findings help to narrow the pool of likely causal explanations for social inequalities in EF skills and may help to identify pupils who are at risk for cognitive, and therefore academic, challenges.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102822"},"PeriodicalIF":9.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145615073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.lindif.2025.102839
Julien S. Bureau , William Gilbert , Frédéric Guay
Motivational theories like self-determination theory help to better understand academic functioning by distinguishing between different types of motivated behaviors. Person-centered analyses, a trending quantitative analytical method, help uncover natural clustering in motivation types among students, which can then be used to predict individual differences in outcomes. However, it is possible that the grouping that naturally occurs when using these analyses entails transformative theoretical implications, beyond a simple description of motivation patterns. Rather, person-centered analyses possibly expose parsimonious and authentic configurations of complex individual differences, in which motivational functioning represents only a subcomponent of a larger cognitive/affective architecture. Results of these analyses are often interpreted in a cursory manner, focusing on how their results align with a theory. A more thorough and humble interpretation of these results may uncover more accurate patterns of individual differences, informing targeted interventions to support learning. This proposition is illustrated with research rooted in self-determination theory.
{"title":"The potential of person-centered analyses to unlock a broader understanding of individual differences in learning","authors":"Julien S. Bureau , William Gilbert , Frédéric Guay","doi":"10.1016/j.lindif.2025.102839","DOIUrl":"10.1016/j.lindif.2025.102839","url":null,"abstract":"<div><div>Motivational theories like self-determination theory help to better understand academic functioning by distinguishing between different types of motivated behaviors. Person-centered analyses, a trending quantitative analytical method, help uncover natural clustering in motivation types among students, which can then be used to predict individual differences in outcomes. However, it is possible that the grouping that naturally occurs when using these analyses entails transformative theoretical implications, beyond a simple description of motivation patterns. Rather, person-centered analyses possibly expose parsimonious and authentic configurations of complex individual differences, in which motivational functioning represents only a subcomponent of a larger cognitive/affective architecture. Results of these analyses are often interpreted in a cursory manner, focusing on how their results align with a theory. A more thorough and humble interpretation of these results may uncover more accurate patterns of individual differences, informing targeted interventions to support learning. This proposition is illustrated with research rooted in self-determination theory.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102839"},"PeriodicalIF":9.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.lindif.2025.102840
Kathryn R. Wentzel
As commentary on the “road ahead” for scholarship on motivation at school, this essay focuses on the need for theorizing and research that recognizes the rich and nuanced characteristics of the social contexts that influence a student's motivation to learn. Recognizing that social contexts are integral to understanding student motivation to learn, I first describe a competence-in-context perspective that illustrates how contexts and competencies provide a foundation for motivation. The contribution of social goal pursuit to motivated action, and the social nature of instructional contexts and belief systems that facilitate goal pursuit are discussed. Socialization experiences that define and influence the development of these motivational belief systems are also proposed. Suggestions for future theorizing and research that recognize the contributions of students' social motivation and the social contexts of schooling to our understanding of students' motivation to learn are offered.
Educational relevance
This article discusses the importance of including the role of social contexts in future theorizing and research on students' motivation to learn. Social contexts are defined with respect to interpersonal relationships, school climates, and developmental and cultural experiences. Contextual influences on social goal setting and beliefs concerning social supports and affordances are described as critical components of motivation to learn.
{"title":"Social foundations of motivation: A pathway forward","authors":"Kathryn R. Wentzel","doi":"10.1016/j.lindif.2025.102840","DOIUrl":"10.1016/j.lindif.2025.102840","url":null,"abstract":"<div><div>As commentary on the “road ahead” for scholarship on motivation at school, this essay focuses on the need for theorizing and research that recognizes the rich and nuanced characteristics of the social contexts that influence a student's motivation to learn. Recognizing that social contexts are integral to understanding student motivation to learn, I first describe a competence-in-context perspective that illustrates how contexts and competencies provide a foundation for motivation. The contribution of social goal pursuit to motivated action, and the social nature of instructional contexts and belief systems that facilitate goal pursuit are discussed. Socialization experiences that define and influence the development of these motivational belief systems are also proposed. Suggestions for future theorizing and research that recognize the contributions of students' social motivation and the social contexts of schooling to our understanding of students' motivation to learn are offered.</div></div><div><h3>Educational relevance</h3><div>This article discusses the importance of including the role of social contexts in future theorizing and research on students' motivation to learn. Social contexts are defined with respect to interpersonal relationships, school climates, and developmental and cultural experiences. Contextual influences on social goal setting and beliefs concerning social supports and affordances are described as critical components of motivation to learn.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102840"},"PeriodicalIF":9.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-20DOI: 10.1016/j.lindif.2025.102838
Hong Lu , Zhengcheng Fan , Frederick K.S. Leung , Xin Chen , Haode Zuo
Despite extensive research exploring the relationship between various spatial reasoning and mathematical abilities, there is rare evidence of their integrated effects and relational specificity. Using the meta-analysis method, this study estimated the spatial reasoning-mathematics correlation and analysed the potential moderating effects of spatial reasoning factors (i.e., spatial visualisation, mental rotation, and spatial orientation), mathematical domains (i.e., numerical, arithmetic, geometric, logical reasoning, and comprehensive math), and age (i.e., preschoolers, children, adolescents, and adults). After integrating 62 studies with 239 effect sizes, a moderate correlation (r = 0.31, 95 % CI [0.29, 0.33]) was identified between spatial reasoning and mathematics; spatial visualisation and spatial orientation showed stronger associations with mathematical ability than mental rotation; comprehensive math, geometry and logical reasoning exhibited superior correlations with spatial reasoning than arithmetic. In addition, no relational differences were detected among preschoolers, children, adolescents, and adults. Theoretical and practical implications of these findings are discussed.
Educational relevance statement
The present study identified a moderate correlation (r = 0.31, 95 % CI [0.29, 0.33]) between spatial reasoning and mathematical ability and supported its underlying specificity. The spatial reasoning-mathematics relationship shifts depending on the demands of particular spatial reasoning and mathematical tasks; however, the age effect needs further scrutiny. Therefore, it is important to acknowledge the multi-dimensional nature of spatial reasoning and mathematics as we delve deeper into their relationship. Especially under the general conceptualisation of mental imagery, spatial transformation and visualisation processing, a fine-grained differentiation across spatial reasoning factors should be adopted. This result justifies and highlights the uniqueness of each spatial reasoning factor and the necessity of its discrimination for spatial training, instruction, or intervention for mathematical improvement purposes.
{"title":"How is spatial reasoning associated with mathematical ability? Evidence based on a meta-analysis","authors":"Hong Lu , Zhengcheng Fan , Frederick K.S. Leung , Xin Chen , Haode Zuo","doi":"10.1016/j.lindif.2025.102838","DOIUrl":"10.1016/j.lindif.2025.102838","url":null,"abstract":"<div><div>Despite extensive research exploring the relationship between various spatial reasoning and mathematical abilities, there is rare evidence of their integrated effects and relational specificity. Using the meta-analysis method, this study estimated the spatial reasoning-mathematics correlation and analysed the potential moderating effects of spatial reasoning factors (i.e., spatial visualisation, mental rotation, and spatial orientation), mathematical domains (i.e., numerical, arithmetic, geometric, logical reasoning, and comprehensive math), and age (i.e., preschoolers, children, adolescents, and adults). After integrating 62 studies with 239 effect sizes, a moderate correlation (<em>r</em> = 0.31, 95 % CI [0.29, 0.33]) was identified between spatial reasoning and mathematics; spatial visualisation and spatial orientation showed stronger associations with mathematical ability than mental rotation; comprehensive math, geometry and logical reasoning exhibited superior correlations with spatial reasoning than arithmetic. In addition, no relational differences were detected among preschoolers, children, adolescents, and adults. Theoretical and practical implications of these findings are discussed.</div></div><div><h3>Educational relevance statement</h3><div>The present study identified a moderate correlation (<em>r</em> = 0.31, 95 % CI [0.29, 0.33]) between spatial reasoning and mathematical ability and supported its underlying specificity. The spatial reasoning-mathematics relationship shifts depending on the demands of particular spatial reasoning and mathematical tasks; however, the age effect needs further scrutiny. Therefore, it is important to acknowledge the multi-dimensional nature of spatial reasoning and mathematics as we delve deeper into their relationship. Especially under the general conceptualisation of mental imagery, spatial transformation and visualisation processing, a fine-grained differentiation across spatial reasoning factors should be adopted. This result justifies and highlights the uniqueness of each spatial reasoning factor and the necessity of its discrimination for spatial training, instruction, or intervention for mathematical improvement purposes.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102838"},"PeriodicalIF":9.0,"publicationDate":"2025-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1016/j.lindif.2025.102837
Nicholas E. Waters , Sammy F. Ahmed , Natasha Chaku , Emily R. Fyfe
Decades of research have demonstrated that the home learning environment (HLE) supports children's math development. Emerging evidence also suggests that children's math skills may influence changes in the HLE, pointing to potential transactional associations across development. Using a national longitudinal sample (N = 1364), we employed random intercept cross-lagged panel modeling to examine transactional associations between the HLE and children's math achievement from early childhood (54 months) to middle childhood (fifth grade), and from middle childhood to adolescence (age 15). Findings revealed that a more enriching HLE at each wave predicted higher math achievement in each ensuing wave. In turn, children's math achievement was also prospectively associated with greater enrichment in the HLE over time. These findings underscore the dynamic interplay between children's academic skills and their home environments and suggest that the HLE may serve as a modifiable target for interventions designed to support math achievement across development.
{"title":"Transactional links between the home learning environment and children's math achievement from early childhood to adolescence","authors":"Nicholas E. Waters , Sammy F. Ahmed , Natasha Chaku , Emily R. Fyfe","doi":"10.1016/j.lindif.2025.102837","DOIUrl":"10.1016/j.lindif.2025.102837","url":null,"abstract":"<div><div>Decades of research have demonstrated that the home learning environment (HLE) supports children's math development. Emerging evidence also suggests that children's math skills may influence changes in the HLE, pointing to potential transactional associations across development. Using a national longitudinal sample (<em>N</em> = 1364), we employed random intercept cross-lagged panel modeling to examine transactional associations between the HLE and children's math achievement from early childhood (54 months) to middle childhood (fifth grade), and from middle childhood to adolescence (age 15). Findings revealed that a more enriching HLE at each wave predicted higher math achievement in each ensuing wave. In turn, children's math achievement was also prospectively associated with greater enrichment in the HLE over time. These findings underscore the dynamic interplay between children's academic skills and their home environments and suggest that the HLE may serve as a modifiable target for interventions designed to support math achievement across development.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102837"},"PeriodicalIF":9.0,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145569371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-08DOI: 10.1016/j.lindif.2025.102821
Miri Barhak-Rabinowitz , Tzur M. Karelitz , Ido Roll
Assessments of Creative Thinking often treat divergent and convergent thinking as separate processes, failing to capture their interaction within iterative problem-solving. The current study introduces an interactive task, ‘Structures’, designed to measure these cognitive processes in tandem. Participants (N = 122) were tasked with designing polygons under constraints and received real-time feedback to refine their solutions. Measures of divergent and convergent thinking were automatically extracted from the variability of feature combinations in the attempted solutions and the consistency in the progress of these attempts. A model that combines both independent measures was best in explaining overall performance. Results also show a negative correlation between divergent and convergent thinking, suggesting a trade-off. Post-hoc analysis suggests a third competency, resource allocation, that oversees the interplay between these. This work highlights the complementary yet competing processes within iterative Creative Thinking and underscores the potential of digital environments to capture higher-order cognitive skills.
Educational relevance statement
This study provides insights into the iterative process of Creative Thinking, a competence increasingly recognized as essential in the 21st century. Our findings emphasize the importance of fostering both divergent and convergent thinking to enhance Creative Thinking, and their balance. We exemplify tracing these sub-skills as participants tackle a difficult challenge and provide a freely available online tool for examination at https://technion.link/miri/polygon/?timer=20&display_timer=on.
{"title":"Measuring the iterative process of creative thinking: Uncovering the interplay between divergent and convergent thinking","authors":"Miri Barhak-Rabinowitz , Tzur M. Karelitz , Ido Roll","doi":"10.1016/j.lindif.2025.102821","DOIUrl":"10.1016/j.lindif.2025.102821","url":null,"abstract":"<div><div>Assessments of Creative Thinking often treat divergent and convergent thinking as separate processes, failing to capture their interaction within iterative problem-solving. The current study introduces an interactive task, ‘Structures’, designed to measure these cognitive processes in tandem. Participants (<em>N</em> = 122) were tasked with designing polygons under constraints and received real-time feedback to refine their solutions. Measures of divergent and convergent thinking were automatically extracted from the variability of feature combinations in the attempted solutions and the consistency in the progress of these attempts. A model that combines both independent measures was best in explaining overall performance. Results also show a negative correlation between divergent and convergent thinking, suggesting a trade-off. Post-hoc analysis suggests a third competency, resource allocation, that oversees the interplay between these. This work highlights the complementary yet competing processes within iterative Creative Thinking and underscores the potential of digital environments to capture higher-order cognitive skills.</div></div><div><h3>Educational relevance statement</h3><div>This study provides insights into the iterative process of Creative Thinking, a competence increasingly recognized as essential in the 21st century. Our findings emphasize the importance of fostering both divergent and convergent thinking to enhance Creative Thinking, and their balance. We exemplify tracing these sub-skills as participants tackle a difficult challenge and provide a freely available online tool for examination at <span><span>https://technion.link/miri/polygon/?timer=20&display_timer=on</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102821"},"PeriodicalIF":9.0,"publicationDate":"2025-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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