Learning and Individual Differences最新文献

{"title":"Explaining teachers' judgement accuracy of students' subjective well-being by students' individual characteristics and the teacher-student relationship","authors":"Carmen L.A. Zurbriggen ,&nbsp;Susanne Schwab ,&nbsp;Isabelle Kalkusch ,&nbsp;Alex Neuhauser ,&nbsp;Andrea Lanfranchi ,&nbsp;Peter Klaver","doi":"10.1016/j.lindif.2025.102754","DOIUrl":"10.1016/j.lindif.2025.102754","url":null,"abstract":"<div><div>This study investigates whether individual student characteristics and the teacher-student relationship can explain teachers' judgment (in)accuracy of three aspects of students' subjective well-being in early primary education. To this end, we analyse the consistency and specificity of teacher reports and self-reports (<em>N</em> = 1582) of Grade 3 students' subjective well-being by employing a multiple-indicator correlated trait-correlated method minus one model, with explanatory variables assessed in Grades 1 and 3. Our findings indicate that the consistency of teacher reports with students' self-reports is relatively low to moderate. Students' gender, academic achievement, intelligence and behavioural skills are significantly associated with the specificity of teacher reports of students' subjective well-being. The teacher-student relationship explains the specificity in teacher reports, with negative associations for conflicts and positive associations for closeness. This study highlights the relevance of behavioural skills and the teacher-student relationship for the alignment between teachers' judgment and self-reports of students' subjective well-being.</div></div><div><h3>Educational relevance and implications statement</h3><div>Accurate teacher judgment of students' subjective well-being is essential for addressing individual differences and for adaptive teaching, which in turn promotes students' academic and socio-emotional development. The findings of this study reveal that teachers' judgment accuracy of students' subjective well-being in Grade 3 is only low to moderate. Students' gender, academic achievement, intelligence and behavioural skills assessed in Grades 1 and 3 significantly relate to teachers' judgment (in-)accuracy of students' subjective well-being. Furthermore, a conflicting teacher-student relationship is negatively associated with teachers' judgment accuracy, while closeness is positively associated. As one of the first studies, our findings point to a nuanced role of student behaviours and teacher-student relationships in shaping teachers' judgments of students' subjective well-being.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102754"},"PeriodicalIF":3.8,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144605702","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}

{"title":"Exploring individual's emotional and autonomous learning profiles in AI-enhanced data-driven language learning: An expanded sor perspective","authors":"Honggang Liu ,&nbsp;Jiqun Fan ,&nbsp;Miaoyue Xia","doi":"10.1016/j.lindif.2025.102753","DOIUrl":"10.1016/j.lindif.2025.102753","url":null,"abstract":"<div><div>Artificial Intelligence (AI) technology has significantly empowered English as a Foreign Language (EFL) education, effectively addressed the individual needs of learners and advanced research into individual differences under Data-Driven Learning (DDL). Artificial Intelligence (AI) has not only transformed pedagogical approaches in EFL education but has also opened new avenues for Data-Driven Learning (DDL) research, particularly in examining learner individuality through advanced data analytics. In AI-enhanced DDL, studies examining learners' emotional profiles through data are notably rare, and the mechanisms by which emotional differences impact language skill development and academic achievement require further investigation. To address this gap, the current study focuses on 753 undergraduate non-English major students from three universities in East China. It investigates the mediating role of learners' autonomy and changes in learning attitudes on the pathways affecting academic engagement and continuance intention. The study extends the application of the SOR theory within AI-enhanced education, elucidating the psychological and emotional pathways through which external stimuli influence individual academic performance. Additionally, it provides insights into students' emotional regulation in AI-enhanced data-driven learning.</div></div><div><h3>Educational relevance and implications</h3><div>Data-Driven Learning (DDL) is an instructional approach that leverages large datasets, typically from corpora, to facilitate individualized language learning, which is also a significant direction for the application of AI in education. By leveraging AI technologies to analyze personal learning data, especially psychological and emotional data, learners can more efficiently plan their self-directed learning. The current study explores the important role of psychological and emotional factors in AI-enhanced DDL under the context of Artificial Intelligence in Education (AIED), drawing on the Stimulus-Organism-Response (S-O-R) theory and Self-Determination Theory (SDT). The findings of this study provide pedagogical insights for teachers in AIED contexts, enhance learners' individual regulatory capabilities during self-directed learning, and offer practical guidance for the development and optimization of AIED-related software.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102753"},"PeriodicalIF":3.8,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144595905","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}

{"title":"Longitudinal relationship between mathematics learning engagement and children's mathematics achievement: Different roles of children's mathematics anxiety and positive parenting style","authors":"Han Xiao ,&nbsp;Jingyuan Yang ,&nbsp;Jingyi Zhang ,&nbsp;Huanyu Li ,&nbsp;Xiao Yu","doi":"10.1016/j.lindif.2025.102747","DOIUrl":"10.1016/j.lindif.2025.102747","url":null,"abstract":"<div><div>To clarify how individual factors (i.e., mathematics learning engagement and mathematics anxiety) and family environmental factors (i.e., parenting style) longitudinally contribute to children's mathematics achievement, 323 Chinese primary school children (<em>M</em>age = 10.02 ± 0.88, 58.51 % girls) were measured at 3 times (June 2021, December 2021 and June 2022). After controlling for gender, age, and T1 mathematics achievement, children's T1 mathematics anxiety played a fully mediating role between T1 mathematics learning engagement and T2 (T3) mathematics achievement. T2 mathematics anxiety and T2 mathematics achievement played fully longitudinal chain mediating roles between T1 mathematics learning engagement and T3 mathematics achievement. Moreover, T2 positive parenting style moderated the path from children's T1 mathematics learning engagement to T2 mathematics anxiety, demonstrating the stress sensitization model of moderation. With China's new educational legislation, these findings provide a glimpse into the crucial mechanisms in the long-term associations related to children's mathematics learning.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102747"},"PeriodicalIF":3.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548429","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}

{"title":"Unraveling the reciprocal effects and micro-development of metacognitive monitoring strategies and monitoring judgments: A micro-genetic diary study","authors":"Marion Händel ,&nbsp;Ulrike Nett ,&nbsp;Donna Bryce ,&nbsp;Markus Dresel","doi":"10.1016/j.lindif.2025.102748","DOIUrl":"10.1016/j.lindif.2025.102748","url":null,"abstract":"<div><div>For successful learning, monitoring one's learning behavior and outcomes is essential. While both the use of monitoring strategies and monitoring judgment accuracy are regarded as indicators for metacognitive monitoring, their joint contribution to metacognitive monitoring and their interrelationship is unclear. The current study investigated how monitoring strategies and monitoring judgments (spontaneous as well as prompted judgments) change micro-genetically and influence each other reciprocally during an exam preparation phase. In this diary study, <em>N</em> = 273 higher education students completed knowledge tests and surveys on five consecutive days. Results indicated interindividual differences in dependence of prior performance and gender for the accuracy of prompted judgments but not for monitoring strategies or spontaneous judgments. Random intercept cross-lagged panel models revealed that higher quality monitoring strategies led to the spontaneous formation of monitoring judgments, while the quality of monitoring strategies and prompted judgment accuracy did not influence each other significantly.</div></div><div><h3>Educational relevance and implications statement</h3><div>This study showed that students metacognitively monitor their learning in an intense exam preparation phase via monitoring strategies as well as spontaneous judgments, regardless of prior performance or gender. As their spontaneous judgments were influenced by the quality of their reported monitoring strategies, the training of monitoring strategies seems an ideal starting point to foster metacognitive monitoring in students.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102748"},"PeriodicalIF":3.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535651","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}

{"title":"Personalizing simulation-based learning in higher education","authors":"Elisabeth Bauer ,&nbsp;Nicole Heitzmann ,&nbsp;Maria Bannert ,&nbsp;Olga Chernikova ,&nbsp;Martin R. Fischer ,&nbsp;Anne C. Frenzel ,&nbsp;Martin Gartmeier ,&nbsp;Sarah I. Hofer ,&nbsp;Doris Holzberger ,&nbsp;Enkelejda Kasneci ,&nbsp;Jenna Koenen ,&nbsp;Christian Kosel ,&nbsp;Stefan Küchemann ,&nbsp;Jochen Kuhn ,&nbsp;Tilman Michaeli ,&nbsp;Birgit J. Neuhaus ,&nbsp;Frank Niklas ,&nbsp;Andreas Obersteiner ,&nbsp;Jürgen Pfeffer ,&nbsp;Michael Sailer ,&nbsp;Frank Fischer","doi":"10.1016/j.lindif.2025.102746","DOIUrl":"10.1016/j.lindif.2025.102746","url":null,"abstract":"<div><div>As digitalization progresses and technologies advance rapidly, digital simulations offer great potential for learning professional practices in contexts such as medical or teacher higher education. The technological advancements increasingly facilitate the personalization of learning support to meet the individual needs of learners, whose diverse prerequisites influence their learning processes, activities, and outcomes. However, systematic approaches to combining technologies with educational theories and evidence are scarce. In this article, we propose to use data on relevant learning prerequisites and learning processes as a basis for personalizing feedback and scaffolding to facilitate learning with simulated practice representations. We connect theoretical concepts with methodological and technical approaches (e.g., using artificial intelligence) for modeling important learner variables as a basis for personalized learning support. The interplay between the learner and the simulation environment is outlined in a conceptual framework which may guide systematic research on personalized learning support in digital simulations.</div></div><div><h3>Educational relevance statement</h3><div>This paper introduces a conceptual framework, which aims to advance personalized simulation-based learning in higher education. Digital simulations can provide tailored learning experiences that adapt to students' individual differences and needs, using artificial intelligence and other technological advances. This approach might have the potential to transform learning in higher education by increasing student engagement and the effectiveness of learning professional knowledge and skills. The framework is discussed along five central questions of personalized learning, which may guide systematic research on how simulations can accommodate learners' diverse prerequisites and processes. In doing so, the framework provides a starting point for interdisciplinary research collaborations aimed at developing design principles for personalized simulation-based learning in higher</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102746"},"PeriodicalIF":3.8,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518329","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}

{"title":"For skilled comprehenders digital reading is a routine task, for unskilled comprehenders it is a problem","authors":"Johannes Naumann ,&nbsp;Liene Brandhuber ,&nbsp;Frank Goldhammer ,&nbsp;Beate Eichmann ,&nbsp;Samuel Greiff","doi":"10.1016/j.lindif.2025.102745","DOIUrl":"10.1016/j.lindif.2025.102745","url":null,"abstract":"<div><div>From theories of task-oriented reading we derived the hypothesis that digital reading has characteristics of a problem for poor comprehenders, while it poses a routine task for skilled comprehenders. It follows that problem solving predicts navigation and digital reading over and above comprehension, and there is an ordinal interaction between comprehension and problem solving. This interaction means that effects of problem solving on navigation and digital reading are stronger in poor, and weaker in skilled comprehenders. Effects of problem solving, comprehension and their interaction on digital reading are assumed to be mediated through navigation. These hypotheses were tested using PISA-Data (<em>N</em> = 13,080), employing two indices of navigation, “Precision” and “Adaptive processing”. Results confirmed our hypotheses with the only exception that Adaptive processing was not predicted by the interaction between problem solving and comprehension. Our results suggest that digital reading constitutes a routine task for skilled, but a problem for unskilled comprehenders.</div></div><div><h3>Educational relevance statement</h3><div>In the 21st century virtually every student and adult is confronted with the task to search for, read, understand and use information on line on a daily basis for personal, educational or occupational purposes. For some readers this task is routine, which they easily accomplish. For others, this task is a complex problem, which they cannot readily solve. The present research shows that good comprehension skill, as taught in advanced reading instruction, puts readers in a position to proficiently find and adequately process information on line. In the face of lacking comprehension skill digital reading becomes a problem, which can be overcome when readers are in command of good problem solving skill. This means that besides efficient reading instruction, designing curricula to improve problem solving skill is vital to teach students how to navigate the digital 21st century information landscape.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102745"},"PeriodicalIF":3.8,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364773","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}

{"title":"“Can (A)I do this task?” The role of AI as a socializer of students' self-beliefs of their abilities","authors":"Thorben Jansen ,&nbsp;Jennifer Meyer ,&nbsp;Johanna Fleckenstein ,&nbsp;Allan Wigfield ,&nbsp;Jens Möller","doi":"10.1016/j.lindif.2025.102731","DOIUrl":"10.1016/j.lindif.2025.102731","url":null,"abstract":"<div><div>Students' beliefs about their own academic abilities – their answers to the question “Can I do this task?” - are crucial to their success. Learning within AI-supported environments, alongside AI agents, influences students' beliefs about their abilities. Studies show enhancing and diminishing influences that remain unexplained by motivation theory, limiting theories' explanatory effect in AI-supported learning environments, and leaving educational technology research without a solid theoretical foundation. The following article specifies the situated expectancy-value theory (SEVT) for students' self-belief formation in the context of an AI-driven society. The expanded theory conceptualizes AI as becoming an artificial socializer, capturing the role of AI as an instrumental tool and social agents making up students' individual environments. Bridging AI and motivational research provides a framework for systematically investigating students' self-beliefs in AI-supported contexts and how educational technology can support positive self-beliefs, considering students' contexts and individual differences.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102731"},"PeriodicalIF":3.8,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331428","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}

{"title":"Exploring the mediating role of academic anxiety in mathematics and reading performance among boys and girls: A comprehensive study of Italian fifth graders","authors":"Sara Caviola ,&nbsp;David Giofrè ,&nbsp;Tommaso Feraco ,&nbsp;Enrico Toffalini ,&nbsp;Katie Allen ,&nbsp;David C. Geary","doi":"10.1016/j.lindif.2025.102726","DOIUrl":"10.1016/j.lindif.2025.102726","url":null,"abstract":"<div><div>There is a long-standing debate regarding the magnitude of gender differences in academic achievement. Using data from a robust and nationally representative sample of 146,227 Italian-fifth-graders, we investigated whether academic anxiety is related to gender differences in mathematics and reading achievement. Across six independent samples, boys had higher performance in mathematics (<em>d</em>s = −0.13 to −0.21) and girls had higher performance in reading (<em>d</em>s = 0.07 to 0.21) and higher test anxiety (<em>d</em>s = 0.30 to 0.37). Meta-analytic procedures indicated these patterns were stable across samples. Path analyses within and across samples suggested about one-third of the academic gender gaps can be accounted for by test anxiety. In particular, with control of test anxiety girls' advantage in reading achievement increased, while boys' advantage in mathematics decreased.</div></div><div><h3>Educational statement</h3><div>The current study provides an extensive exploration of how test anxiety potentially influences gender differences in mathematics and reading achievement. Test anxiety appears to lower girls' performance on achievement tests and thus underestimating their advantages in reading and overestimating boys' advantages in mathematics. One implication is that efforts to reduce test anxiety will enhance performance on achievement tests, especially for test-anxious girls, and through this provide more accurate estimates of academic competencies.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102726"},"PeriodicalIF":3.8,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313336","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}

{"title":"How does prior knowledge affect learning? A review of 16 mechanisms and a framework for future research","authors":"Michael Schneider,&nbsp;Bianca A. Simonsmeier","doi":"10.1016/j.lindif.2025.102744","DOIUrl":"10.1016/j.lindif.2025.102744","url":null,"abstract":"<div><div>The knowledge-is-power hypothesis posits that individual differences in domain-specific prior knowledge are among the strongest positive determinants of learning. However, a large recent meta-analysis found that the correlations between prior knowledge and knowledge gains had a mean of zero and a large range. This Prior Knowledge Paradox demonstrates the need to understand better how prior knowledge affects learning. We give an integrative review of 16 learning processes mediating the effects of prior knowledge on learning outcomes in learners, for example, encoding, chunking, comprehension, interest, cognitive load, meta-cognition, transfer, and interference. Each process is moderated by further variables, such as content domain, learner characteristics, or instruction. We summarize these findings in the Multiple Moderated Mediations (Triple-M) framework for future research, which emphasizes that a full understanding of the effect of prior knowledge on learning requires the joint consideration of the multiple cognitive and motivational learning mechanisms and moderating influences on them.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102744"},"PeriodicalIF":3.8,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144306335","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}

{"title":"Bringing learners into focus: A systematic review of learner characteristics in AR-supported STEM education","authors":"Zoya Kozlova,&nbsp;Katharina M. Bach,&nbsp;Peter A. Edelsbrunner,&nbsp;Sarah I. Hofer","doi":"10.1016/j.lindif.2025.102727","DOIUrl":"10.1016/j.lindif.2025.102727","url":null,"abstract":"<div><div>To understand how AR effects on STEM learning depend on individual differences, it is essential to follow the ATI (Aptitude-Treatment Interaction) perspective and investigate interactions between individual differences and AR- vs. non-AR conditions. This systematic review explored the extent to which individual characteristics are examined in AR research as predictors to further review if and how AR research in STEM education follows an ATI approach. Our findings reveal that from 2013 to 2022, <em>k</em> = 38 studies investigated the role of individual variables as predictors with only <em>k</em> = 5 studies considering how individual differences interact with AR vs. non-AR conditions. Spatial ability emerged as the most frequently studied learner characteristic in ATI-AR research, yet its impact on learning outcomes remains inconclusive. We discuss possible reasons for this gap and propose solutions, offering a study design framework to conduct AR studies considering the ATI perspective.</div></div>","PeriodicalId":48336,"journal":{"name":"Learning and Individual Differences","volume":"122 ","pages":"Article 102727"},"PeriodicalIF":3.8,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279086","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}