Pub Date : 2026-01-01Epub Date: 2025-11-07DOI: 10.1016/j.lindif.2025.102834
Dan L. Dinsmore , Luke K. Fryer
Many genAI (generative Artificial Intelligence) enthusiasts and much of the broader public see genAI as a substantial force for good within education. Unfortunately, some of those calling for or directly introducing genAI into formal education fail to fully understand one or both of the following realities: a. what genAI's knowledge is, b. how humans learn in any given domain of knowledge. The failure to understand and therefore engage with these foundations for genAI use in education has consequences for students internationally. This paper addresses this gap by considering how genAI (in its current form) is useful as a learning tool. The Model of Domain Learning is provided as one means of recursively engaging with this question regarding learning in academic domains as genAI continues to grow and change.
Educational relevance statement
This manuscript addresses the cognitive processing that underlies learning and must intersect with any contribution genAI makes to educational processes. Consistent with longstanding models, we argue that students' prior knowledge is foundational when determining when and how our current genAI are useful to students.
{"title":"What does current genAI actually mean for student learning?","authors":"Dan L. Dinsmore , Luke K. Fryer","doi":"10.1016/j.lindif.2025.102834","DOIUrl":"10.1016/j.lindif.2025.102834","url":null,"abstract":"<div><div>Many genAI (generative Artificial Intelligence) enthusiasts and much of the broader public see genAI as a substantial force for good within education. Unfortunately, some of those calling for or directly introducing genAI into formal education fail to fully understand one or both of the following realities: a. what genAI's knowledge is, b. how humans learn in any given domain of knowledge. The failure to understand and therefore engage with these foundations for genAI use in education has consequences for students internationally. This paper addresses this gap by considering how genAI (in its current form) is useful as a learning tool. The Model of Domain Learning is provided as one means of recursively engaging with this question regarding learning in academic domains as genAI continues to grow and change.</div></div><div><h3>Educational relevance statement</h3><div>This manuscript addresses the cognitive processing that underlies learning and must intersect with any contribution genAI makes to educational processes. Consistent with longstanding models, we argue that students' prior knowledge is foundational when determining when and how our current genAI are useful to students.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102834"},"PeriodicalIF":9.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468167","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 : 2026-01-01Epub Date: 2025-10-24DOI: 10.1016/j.lindif.2025.102807
Xiao-Yin Chen , Emily Q. Rosenzweig
Similar role models can be powerful tools to motivate participation in science, technology, engineering, and math (STEM) disciplines, but it is unclear what types of similarity are most important to students' motivation. The current study investigated the different ways college students (n = 1185) perceived similarity to STEM role models and how different perceptions of similarity predicted students' STEM career motivation. We assessed overall trends as well as unique patterns among marginalized and non-marginalized gender and racial/ethnic groups in STEM. Perceiving academic similarity to role models positively and robustly predicted students' STEM career motivation, whereas perceiving demographic similarity to role models played a more limited role. Perceiving similar academic efforts to role models seemed to be especially important for motivating students from marginalized gender and racial/ethnic groups in STEM. Findings have important implications for how to leverage role models in college interventions designed to promote STEM motivation and career participation.
Educational relevance and implications statement
Though role models have been shown to be powerful tools in shaping motivation in many science, technology, engineering, and math (STEM) disciplines, not all STEM role models are equally powerful motivators to college students. Our results suggest that role models perceived as academically similar (i.e., in terms of academic abilities, interests, or efforts) may positively support college students' competence-related beliefs and values for pursuing STEM careers. Students' gender and racial/ethnic background also shaped how they related to and felt motivated by STEM role models. Presenting students with role models who put forth similar academic efforts to students may be especially helpful in supporting motivation among students from historically marginalized gender and racial/ethnic groups in STEM.
{"title":"Investigating academic and demographic similarities to career role models for motivating diverse college students in STEM","authors":"Xiao-Yin Chen , Emily Q. Rosenzweig","doi":"10.1016/j.lindif.2025.102807","DOIUrl":"10.1016/j.lindif.2025.102807","url":null,"abstract":"<div><div>Similar role models can be powerful tools to motivate participation in science, technology, engineering, and math (STEM) disciplines, but it is unclear <em>what types</em> of similarity are most important to students' motivation. The current study investigated the different ways college students (<em>n</em> = 1185) perceived similarity to STEM role models and how different perceptions of similarity predicted students' STEM career motivation. We assessed overall trends as well as unique patterns among marginalized and non-marginalized gender and racial/ethnic groups in STEM. Perceiving academic similarity to role models positively and robustly predicted students' STEM career motivation, whereas perceiving demographic similarity to role models played a more limited role. Perceiving similar academic efforts to role models seemed to be especially important for motivating students from marginalized gender and racial/ethnic groups in STEM. Findings have important implications for how to leverage role models in college interventions designed to promote STEM motivation and career participation.</div></div><div><h3>Educational relevance and implications statement</h3><div>Though role models have been shown to be powerful tools in shaping motivation in many science, technology, engineering, and math (STEM) disciplines, not all STEM role models are equally powerful motivators to college students. Our results suggest that role models perceived as academically similar (i.e., in terms of academic abilities, interests, or efforts) may positively support college students' competence-related beliefs and values for pursuing STEM careers. Students' gender and racial/ethnic background also shaped how they related to and felt motivated by STEM role models. Presenting students with role models who put forth similar academic efforts to students may be especially helpful in supporting motivation among students from historically marginalized gender and racial/ethnic groups in STEM.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102807"},"PeriodicalIF":9.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145366133","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub 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":"2026-01-01","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 : 2026-01-01Epub Date: 2025-10-27DOI: 10.1016/j.lindif.2025.102819
Susan Sonnenschein , Michele Stites , Besjanë Krasniqi
In spring 2020, an estimated 55.1 million children in the United States experienced school closures related to COVID-19 (Education Week, 2020). As a result of these closures, 93 % of families reported their children's schools transitioned to virtual learning (U.S. Census, 2021). Research has found significant gaps in students' learning because of these COVID-19 pandemic school closures. This paper describes the educational areas most negatively impacted by the COVID-19 school closures as identified by families and schools. The negative impacts were especially significant for students of color, families from near or below the poverty line, and students with disabilities. As discussed below, students' learning during COVID-19 was most negatively impacted by lack of internet/technology, quality of and frequency of engagement in instruction, and attendance at virtual learning sessions. The article presents recommendations for decreasing the learning gaps left in the wake of the COVID-19 school closures and areas of future research inquiry.
Educational relevance
This paper examines the complex impacts of the COVID-19 pandemic on children's learning. We focus specifically on how systemic inequities were made worse during school closures. This review of the literature examines why specific student populations experienced more significant learning disruptions. Actionable recommendations, including differentiated instruction and the integration of UDL principles, are provided.
{"title":"Factors affecting children's learning during COVID-19","authors":"Susan Sonnenschein , Michele Stites , Besjanë Krasniqi","doi":"10.1016/j.lindif.2025.102819","DOIUrl":"10.1016/j.lindif.2025.102819","url":null,"abstract":"<div><div>In spring 2020, an estimated 55.1 million children in the United States experienced school closures related to COVID-19 (Education Week, 2020). As a result of these closures, 93 % of families reported their children's schools transitioned to virtual learning (U.S. Census, 2021). Research has found significant gaps in students' learning because of these COVID-19 pandemic school closures. This paper describes the educational areas most negatively impacted by the COVID-19 school closures as identified by families and schools. The negative impacts were especially significant for students of color, families from near or below the poverty line, and students with disabilities. As discussed below, students' learning during COVID-19 was most negatively impacted by lack of internet/technology, quality of and frequency of engagement in instruction, and attendance at virtual learning sessions. The article presents recommendations for decreasing the learning gaps left in the wake of the COVID-19 school closures and areas of future research inquiry.</div></div><div><h3>Educational relevance</h3><div>This paper examines the complex impacts of the COVID-19 pandemic on children's learning. We focus specifically on how systemic inequities were made worse during school closures. This review of the literature examines why specific student populations experienced more significant learning disruptions. Actionable recommendations, including differentiated instruction and the integration of UDL principles, are provided.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102819"},"PeriodicalIF":9.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145420040","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 : 2026-01-01Epub Date: 2025-11-03DOI: 10.1016/j.lindif.2025.102823
Qiyang Gao , Tianyu Xu , Peiyao Chen , Ruru Zhang , Zhenlin Wang
This study presents a longitudinal evidence of co-occurring developmental changes in theory of mind (ToM) and reading comprehension in a group of 159 children (ages 8–10; M = 9.96, SD = 0.93; 92 girls). We tracked participants over one year using identical measures of ToM, narrative reading comprehension (NRC), and expository reading comprehension (ERC) at two time points. Applying a Latent Change Score (LCS) model, we found that individual differences in ToM and NRC not only influenced each other's growth over time but were also significantly correlated at both initial measurement and in their change scores. However, only initial ToM was associated with gains in ERC during the one-year interval, but not vice versa. These findings suggest a reciprocal causal relationship between socio-cognitive and academic development and highlight the importance of integrating both domains in educational interventions.
Educational relevance statement
Our findings demonstrate that Theory of Mind (ToM) and narrative reading comprehension (NRC) are reciprocally related over time, suggesting that strengthening one domain can accelerate growth in the other. Importantly, children with stronger initial abilities in either ToM or NRC experienced greater gains in the other domain, indicating the risk or widening achievement gaps without early support. Moreover, ToM predicted later gains in expository reading comprehension (ERC), underscoring its role in supporting comprehension of increasingly complex academic texts. These results suggest that integrating ToM and reading comprehension training within educational practice can enhance cognitive and academic development in tandem. Such integration may be particularly impactful for students at risk of early learning difficulties, offering a promising direction for targeted, developmentally informed interventions.
{"title":"Reciprocal association between theory of mind and reading comprehension of narrative (but not expository) text in middle childhood: A latent change score approach","authors":"Qiyang Gao , Tianyu Xu , Peiyao Chen , Ruru Zhang , Zhenlin Wang","doi":"10.1016/j.lindif.2025.102823","DOIUrl":"10.1016/j.lindif.2025.102823","url":null,"abstract":"<div><div>This study presents a longitudinal evidence of co-occurring developmental changes in theory of mind (ToM) and reading comprehension in a group of 159 children (ages 8–10; <em>M</em> = 9.96, <em>SD</em> = 0.93; 92 girls). We tracked participants over one year using identical measures of ToM, narrative reading comprehension (NRC), and expository reading comprehension (ERC) at two time points. Applying a Latent Change Score (LCS) model, we found that individual differences in ToM and NRC not only influenced each other's growth over time but were also significantly correlated at both initial measurement and in their change scores. However, only initial ToM was associated with gains in ERC during the one-year interval, but not vice versa. These findings suggest a reciprocal causal relationship between socio-cognitive and academic development and highlight the importance of integrating both domains in educational interventions.</div></div><div><h3>Educational relevance statement</h3><div>Our findings demonstrate that Theory of Mind (ToM) and narrative reading comprehension (NRC) are reciprocally related over time, suggesting that strengthening one domain can accelerate growth in the other. Importantly, children with stronger initial abilities in either ToM or NRC experienced greater gains in the other domain, indicating the risk or widening achievement gaps without early support. Moreover, ToM predicted later gains in expository reading comprehension (ERC), underscoring its role in supporting comprehension of increasingly complex academic texts. These results suggest that integrating ToM and reading comprehension training within educational practice can enhance cognitive and academic development in tandem. Such integration may be particularly impactful for students at risk of early learning difficulties, offering a promising direction for targeted, developmentally informed interventions.</div><div>Preregistration: <span><span>https://doi.org/10.17605/OSF.IO/69Q5R</span><svg><path></path></svg></span></div><div>Data: <span><span>https://data.mendeley.com/datasets/zfzd852xpg/1</span><svg><path></path></svg></span></div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"125 ","pages":"Article 102823"},"PeriodicalIF":9.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468168","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 : 2026-01-01Epub 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.
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Pub Date : 2026-01-01Epub 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":"2026-01-01","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}
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