Learning and Individual Differences最新文献_第5页

Separating prior knowledge from acquired knowledge: An individual differences analysis of PISA - learning in the digital world 从获得的知识中分离先验知识：数字世界中PISA学习的个体差异分析

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-30 DOI: 10.1016/j.lindif.2025.102820

Leonard Tetzlaff , Lothar Persic-Beck , Ulf Kröhne , Carolin Hahnel , Daniel Schiffner , Frank Goldhammer

The innovative PISA domain “Learning in the digital world (LDW)” integrates the assessment of knowledge and skills with opportunities for learning. To investigate whether learning took place during the assessment, we analyzed data from 737 German PISA 2022 students and modeled individual differences in test performance that could not be explained by students'prior knowledge and skills. We then related these differences to learning activity (the use of worked examples during a task) and learning prerequisites (general intelligence and mastery orientation) to validate their interpretation as performance based on knowledge and skills acquired during the task. We found substantial remaining variance in performance after controlling for prior knowledge (21 % of variance). Significant relationships of these differences with both learning prerequisites and learning activity provide further evidence for interpreting found differences as a result of learning during the task.

Educational relevance statement

This study provides initial evidence that a learning process takes place during PISA-LDW assessments. Students that made use of the provided learning opportunities and/or have high intelligence consistently performed better than would be expected based on their prior knowledge. The use of learning opportunities was related to the learning goal orientation of students. Digital environments should be designed in a way that is conducive to learning goal orientations and provide explicit learning opportunitites.

创新的PISA领域“数字世界中的学习（LDW）”将知识和技能评估与学习机会相结合。为了调查在评估过程中是否发生了学习，我们分析了737名德国PISA 2022学生的数据，并模拟了学生先前的知识和技能无法解释的测试表现的个体差异。然后，我们将这些差异与学习活动（在任务中使用工作示例）和学习先决条件（一般智力和掌握导向）联系起来，以验证他们对基于任务中获得的知识和技能的表现的解释。在控制了先验知识（21%的方差）之后，我们发现了大量剩余的性能方差。这些差异与学习先决条件和学习活动的显著关系为解释任务中学习的结果所发现的差异提供了进一步的证据。教育相关性声明本研究提供了初步证据，证明在PISA-LDW评估期间发生了学习过程。利用提供的学习机会和/或具有高智商的学生始终比基于他们先前知识的预期表现得更好。学习机会的利用与学生的学习目标取向有关。数字环境的设计应有利于学习目标导向，并提供明确的学习机会。

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引用次数: 0

Emotional intelligence predicts initial status and growth of reading comprehension in primary school students 情绪智力预测小学生阅读理解的初始状态和成长

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-28 DOI: 10.1016/j.lindif.2025.102814

Liyan Yu , Catherine McBride , Xiuhong Tong

This study examined the associations between emotional intelligence and the initial level and growth rate of reading comprehension in narrative and non-narrative texts. A sample of 689 Chinese third-grade students (49.49 % girls; M_age = 9.23 years, SD = 0.66) from eight primary schools was assessed over three years. At Time 1, students completed measures of emotional intelligence, word reading, and listening comprehension. Reading comprehension was assessed at all three time points. Latent growth curve modeling revealed that emotional intelligence at Time 1 predicted the initial level of reading comprehension for both narrative and non-narrative texts but only predicted the growth rate for narrative texts. These findings highlight the importance of emotional intelligence in reading comprehension, particularly for narrative texts, and emphasize the need to incorporate it into reading comprehension models.

Educational relevance statement

The findings that emotional intelligence is associated with initial reading comprehension for both narrative and non-narrative texts and that it uniquely predicts growth for narrative texts, highlights new opportunities for enhancing reading instruction. Gender differences, with boys showing lower initial comprehension and slower growth, underscore the need for gender-sensitive instructional strategies. These results suggest that incorporating emotional intelligence into literacy education and adapting instruction based on text genre and gender may lead to more effective and equitable reading outcomes for diverse learners.

本研究考察了情绪智力与叙事文本和非叙事文本阅读理解的初始水平和增长速度之间的关系。对来自8所小学的689名中国三年级学生（49.49%为女生；年龄= 9.23岁，SD = 0.66）进行了为期三年的评估。在第一阶段，学生们完成了情商、单词阅读和听力理解的测试。阅读理解在所有三个时间点进行评估。隐性增长曲线模型显示，时间1的情绪智力可以预测叙事文本和非叙事文本的初始阅读理解水平，但只能预测叙事文本的阅读理解增长率。这些发现强调了情商在阅读理解中的重要性，特别是对于叙事文本，并强调了将其纳入阅读理解模型的必要性。教育相关性陈述：研究发现情绪智力与叙事和非叙事文本的初始阅读理解有关，并且它独特地预测了叙事文本的发展，强调了加强阅读教学的新机会。性别差异，男孩表现出较低的初始理解能力和较慢的成长，强调了对性别敏感的教学策略的必要性。这些结果表明，将情商纳入识字教育，并根据文本类型和性别调整教学，可能会为不同的学习者带来更有效和公平的阅读结果。

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引用次数: 0

Mapping the intellectual landscape of educational psychology: Citation rankings and network structures of 60 journals, scholars, and institutions 绘制教育心理学的智力景观：60种期刊、学者和机构的引文排名和网络结构

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-28 DOI: 10.1016/j.lindif.2025.102816

Jinran Wu , Herbert W. Marsh , Jiesi Guo , Johnmarshall Reeve , Reinhard Pekrun , Theresa Dicke , Hye-Ryen Jang , Geetanjali Basarkod

Understanding how influence is structured within educational psychology is critical for advancing research quality, equity, and impact. We introduce a replicable, field-sensitive framework that combines (a) the Educational Psychology H-index (EP-H), which isolates within-field impact, and (b) a cross-citation network mapping journals and subfields. Using 27,482 articles from 60 Web of Science educational-psychology journals (2015–2024), we rank researchers, institutions, countries, and journals, examine convergent validity with established metrics, and identify thematic clusters centered on motivation, learning strategies, emotions, and cognition. Results reveal concentrated influence alongside meaningful international contributions. Leading journals include Journal of Educational Psychology and Educational Psychology Review, with Learning and Individual Differences showing strong connectivity across cognitive, emotional, and motivational subfields. The approach clarifies how citation volume and structural position jointly influence visibility, provides transparent tools for editors and institutions, and can be adapted to other disciplines where disciplinary context is relevant.

Educational relevance and implications

This study provides editors, institutions, and researchers with a clear understanding of who and what influences educational psychology. Using a field-specific Educational Psychology H-index (EP-H) and a cross-citation network, we identify journals that connect subfields and scholars who are most visible within the discipline. These tools help early-career scholars select venues and assist departments in evaluating contributions in core areas of educational psychology. The approach is transparent, replicable, and adaptable to related areas of education research.

了解影响在教育心理学中是如何构成的，对于提高研究质量、公平性和影响至关重要。我们引入了一个可复制的、领域敏感的框架，该框架结合了(a)教育心理学h指数（EP-H），该指数隔离了领域内的影响，以及(b)交叉引用网络映射期刊和子领域。使用来自60个Web of Science教育心理学期刊（2015-2024）的27,482篇文章，我们对研究人员、机构、国家和期刊进行了排名，用既定的指标检查了收敛效度，并确定了以动机、学习策略、情感和认知为中心的主题集群。结果显示，除了有意义的国际贡献外，还具有集中的影响力。包括《教育心理学杂志》和《教育心理学评论》在内的主要期刊，《学习与个体差异》显示了认知、情感和动机子领域之间的紧密联系。该方法阐明了引文量和结构位置如何共同影响可见性，为编辑和机构提供了透明的工具，并且可以适用于与学科背景相关的其他学科。教育的相关性和意义本研究为编辑、机构和研究人员提供了一个清晰的认识，谁和什么影响教育心理学。使用特定领域的教育心理学h指数（EP-H）和交叉引用网络，我们确定了连接子领域和在该学科中最引人注目的学者的期刊。这些工具帮助早期职业学者选择场所，并协助院系评估教育心理学核心领域的贡献。这种方法是透明的、可复制的，并且适用于教育研究的相关领域。

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引用次数: 0

Enhancing digital reading comprehension through feedback messages: A large and long-term dynamic approach with secondary school students 通过反馈信息提高数字阅读理解能力：一项针对中学生的大型长期动态方法

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-27 DOI: 10.1016/j.lindif.2025.102818

Amelia Mañá , Lidia Altamura , Pablo Delgado , Laura Gil , Mario Romero-Palau , Marian Serrano-Mendizábal , Cristina Vargas , Ladislao Salmerón

Many high school students struggle to efficiently read complex digital documents that require different self-regulating processes, such as identifying main ideas or integrating multiple documents. To foster these processes, we designed and tested a long-term dynamic approach in which 700 students from grades 7 to 10 answered adjunct comprehension questions and received immediate feedback either about the performance (corrective) or about different processes and strategies to answer the questions (elaborated). Surprisingly, the effect of feedback on comprehension scores varied across samples. Elaborated feedback had a positive impact on 7th–8th grade students' self-regulation and comprehension scores, whereas Corrective Feedback yielded greater improvements in these domains for 9th–10th grade students. The effects were partially mediated by students' reviewing time in both samples. We discuss the need to adapt dynamic assessment interventions to students' educational level.

Educational relevance statement

In today's digital age, high school students face significant challenges in self-regulating their reading comprehension process. Addressing this particular concern, the current study examines how answering questions and receiving feedback while reading digital texts can enhance self-regulation and comprehension in high school students. Of particular relevance for instruction was the fact that different types of feedback had opposing effects depending on students grade level. Providing elaborated information aimed at guiding text processing to solve a comprehension question (e.g., “It is important to combine the information in the text with your own knowledge”) supported students in their early years of high school, while simply stating whether the response was correct or incorrect worked better for those in the final years of high school. These results can help educators make informed decisions about designing feedback in digital environments to promote self-regulation and comprehension.

许多高中生很难有效地阅读复杂的数字文件，这些文件需要不同的自我调节过程，比如识别主要思想或整合多个文件。为了促进这些过程，我们设计并测试了一种长期动态方法，让700名7至10年级的学生回答辅助理解问题，并立即收到关于表现（纠正）或回答问题的不同过程和策略（阐述）的反馈。令人惊讶的是，反馈对理解分数的影响因样本而异。精细化反馈对7 - 8年级学生的自我调节和理解得分有积极影响，而纠正性反馈对9 - 10年级学生的自我调节和理解得分有更大的影响。在两个样本中，学生的复习时间都部分介导了这种效应。我们讨论了适应学生教育水平的动态评估干预的必要性。在当今的数字时代，高中生在自我调节阅读理解过程中面临着重大挑战。为了解决这一特殊问题，当前的研究探讨了在阅读数字文本时回答问题和接受反馈如何提高高中生的自我调节和理解能力。与教学特别相关的是，根据学生的年级水平不同，不同类型的反馈会产生相反的效果。提供旨在指导文本处理的详细信息来解决理解问题（例如，“将文本中的信息与你自己的知识结合起来是很重要的”）对高中早期的学生很有帮助，而简单地说明回答是正确的还是错误的对高中最后几年的学生更有效。这些结果可以帮助教育工作者做出明智的决定，在数字环境中设计反馈，以促进自我调节和理解。

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引用次数: 0

Factors affecting children's learning during COVID-19 COVID-19期间影响儿童学习的因素

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-27 DOI: 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.

2020年春季，估计有5510万美国儿童因COVID-19而关闭学校（2020年教育周）。由于这些关闭，93%的家庭报告他们孩子的学校过渡到虚拟学习（美国人口普查，2021年）。研究发现，由于这些学校因COVID-19大流行而关闭，学生的学习出现了重大差距。本文描述了由家庭和学校确定的受COVID-19学校关闭影响最大的教育领域。对有色人种学生、接近或低于贫困线的家庭以及残疾学生的负面影响尤为显著。如下所述，在2019冠状病毒病疫情期间，缺乏互联网/技术、教学质量和参与频率以及参加虚拟学习课程对学生的学习产生了最大的负面影响。本文提出了减少COVID-19学校关闭后留下的学习差距的建议和未来研究探究的领域。本文探讨了新冠肺炎疫情对儿童学习的复杂影响。我们特别关注在学校关闭期间，系统性不平等是如何恶化的。这篇文献综述探讨了为什么特定的学生群体经历了更显著的学习中断。提出了可操作的建议，包括差异化指导和UDL原则的整合。

{"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":"2025-10-27","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}

引用次数: 0

Investigating academic and demographic similarities to career role models for motivating diverse college students in STEM 调查学术和人口统计学上职业榜样的相似之处，以激励不同的STEM大学生

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-24 DOI: 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.

相似的榜样可以成为激励学生参与科学、技术、工程和数学（STEM）学科的有力工具，但目前尚不清楚哪种类型的相似性对学生的动机最重要。目前的研究调查了大学生（n = 1185）对STEM角色榜样的不同感知方式，以及对相似性的不同感知如何预测学生的STEM职业动机。我们评估了STEM中边缘化和非边缘化性别和种族/族裔群体的总体趋势以及独特模式。感知与榜样的学术相似性对学生的STEM职业动机有积极而有力的预测作用，而感知与榜样的人口相似性对学生STEM职业动机的作用则较为有限。在STEM中，看到与榜样相似的学术努力似乎对于激励来自边缘化性别和种族/民族群体的学生尤为重要。研究结果对于如何在旨在促进STEM动机和职业参与的大学干预措施中利用榜样具有重要意义。教育相关性和影响陈述尽管在许多科学、技术、工程和数学（STEM）学科中，榜样被证明是塑造动机的有力工具，但并不是所有的STEM榜样对大学生来说都是同样强大的激励因素。我们的研究结果表明，被认为在学术上相似的榜样（即在学术能力、兴趣或努力方面）可能会积极支持大学生追求STEM职业的能力相关信念和价值观。学生的性别和种族/民族背景也影响了他们与STEM榜样的关系，并受到他们的激励。向学生展示与他们在学术上付出类似努力的榜样，可能特别有助于鼓励来自历史上被边缘化的性别和种族/民族群体的学生学习STEM。

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引用次数: 0

Mathematics motivation, achievement, and gender inequality in social intuitions: Unpacking gender differences in STEM career expectations across 75 societies 数学动机、成就和社会直觉中的性别不平等：在75个社会中分析STEM职业期望的性别差异

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-21 DOI: 10.1016/j.lindif.2025.102815

Wei Wu , Yanan Zhang

Gender disparities in STEM career expectations emerge early and remain persistent worldwide. Drawing on PISA 2022 data from 75 societies (N = 578,627), this study examines how mathematics-related motivation and achievement interact with gender inequality in social institutions. Results indicate that males consistently report higher STEM career expectations than females. Intrinsic value in mathematics widens the gender gap, whereas academic achievement exerts a powerful equalizing effect that, at high levels, allows females to surpass males. Greater gender inequality in social institutions is linked to higher overall STEM expectations but simultaneously amplifies gender gaps. The equalizing effect of achievement is intensified in contexts of stronger family discrimination and restricted civil liberties but weakened when physical integrity is constrained. Conversely, the gap-widening effect of intrinsic value is attenuated under limited access to resources. Findings underscore the need for gender-responsive educational policies that address both individual processes and institutional conditions.

Educational impact and implications statement

This study demonstrates that mathematics-related motivation and achievement interact with gender inequality in social institutions to shape adolescents' STEM career expectations. While intrinsic value tends to widen gender gaps, high levels of academic achievement can act as an equalizer, underscoring the importance of supporting female students' achievement pathways. The moderating role of gender inequality in social institutions indicates that classroom interventions alone are insufficient; broader societal efforts to reduce restrictions on physical integrity are also essential to advance gender equity in STEM career expectations. The weakening of motivational effects under limited resources and assets highlights the need to ensure equitable access to STEM career opportunities. Together, these insights call for gender-responsive strategies and policies that enhance academic achievement, address structural barriers, and foster inclusive environments that encourage both female and male students to pursue STEM careers.

STEM职业期望中的性别差异很早就出现，并在世界范围内持续存在。利用来自75个社会（N = 578,627）的PISA 2022数据，本研究考察了与数学相关的动机和成就如何与社会机构中的性别不平等相互作用。结果表明，男性对STEM职业的期望一直高于女性。数学的内在价值扩大了性别差距，而学术成就则发挥了强大的平衡效应，在较高水平上，女性可以超越男性。社会机构中更大的性别不平等与更高的STEM总体期望有关，但同时也扩大了性别差距。在家庭歧视加剧和公民自由受到限制的情况下，成就的平衡效应会增强，但在身体完整性受到限制的情况下，成就的平衡效应会减弱。相反，在资源获取受限的情况下，内在价值的差距拉大效应减弱。调查结果强调有必要制定兼顾个人进程和体制条件的促进性别平等的教育政策。本研究表明，与数学相关的动机和成就与社会机构中的性别不平等相互作用，塑造了青少年的STEM职业期望。虽然内在价值往往会扩大性别差距，但高水平的学业成绩可以起到均衡器的作用，强调了支持女学生成就之路的重要性。性别不平等在社会制度中的调节作用表明，课堂干预本身是不够的；更广泛的社会努力减少对人身安全的限制，对于促进STEM职业期望中的性别平等也至关重要。在资源和资产有限的情况下，激励效应的减弱凸显了确保公平获得STEM职业机会的必要性。总之，这些见解要求制定促进性别平等的战略和政策，以提高学业成绩，消除结构性障碍，并营造包容性环境，鼓励男女学生追求STEM职业。

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引用次数: 0

The developmental interplay between household chaos and educational achievement from age 9 through 16 years: A genetically sensitive study 从9岁到16岁，家庭混乱与教育成就之间的发展相互作用：一项基因敏感研究

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-20 DOI: 10.1016/j.lindif.2025.102811

Sophie von Stumm , Alexandra Starr , Ivan Voronin , Margherita Malanchini

We tested whether associations between household chaos, which refers to confusion and disorganisation in family homes, and educational achievement are confounded by genetics and family socioeconomic status (SES). We modelled the developmental interplay between chaos and achievement, including their reverse association (i.e., achievement → chaos), and its aetiology in up to 7591 twin pairs (49 % female), who were born in the mid-90s in the UK and assessed at age 9, 12, and 16 years. Associations between household chaos and educational achievement were consistently negative, bidirectional, and of small effect sizes across ages. These associations were best explained by genetic and environmental confounding. Family SES accounted for most of the confounding in the predictions from achievement to chaos; for the reverse, environments shared within families but distinct from SES were implied. Our findings suggest that long-term associations between children's experiences of household chaos and educational achievement are modest and non-causal.

Educational relevance and implications statement

Children's differences in educational achievement are evident from the first day of school. Understanding why children differ in educational achievement is key to improving their life chances. Here, we tested if a specific characteristic of children's rearing environment – the level of chaos that they experience in their family homes – predicted their educational achievement. We found that chaos and achievement were linked in both directions: Chaos influenced achievement, and achievement influenced chaos. However, these bidirectional influences did not reflect causal mechanisms. Instead, children's differences in the experience of chaos and educational achievement appear to have the same origin. Our study suggests that changing children's experience of chaos is unlikely to help better their educational achievement.

我们测试了家庭混乱（指家庭中的混乱和无序）与教育成就之间的联系是否会受到遗传和家庭社会经济地位（SES）的影响。我们模拟了混乱与成就之间的发展相互作用，包括它们之间的反向关联（即成就→混乱），以及其病因学，研究对象为7591对双胞胎（49%为女性），这些双胞胎出生在90年代中期的英国，分别在9岁、12岁和16岁时进行了评估。家庭混乱与教育成就之间的联系始终是负向的、双向的，并且在年龄上的影响很小。这些关联最好的解释是遗传和环境的混杂。家庭经济地位在从成就到混乱的预测中占了大部分的混淆；反之，则暗示了家庭内部共享但不同于SES的环境。我们的研究结果表明，儿童的家庭混乱经历与教育成就之间的长期联系是适度的，非因果关系。从上学的第一天起，孩子们在教育成就上的差异就很明显。了解孩子们在教育成就上的差异是改善他们生活机会的关键。在这里，我们测试了孩子的养育环境的特定特征——他们在家庭中经历的混乱程度——是否预测了他们的教育成就。我们发现混乱和成就在两个方向上都有联系：混乱影响成就，成就影响混乱。然而，这些双向影响并没有反映因果机制。相反，孩子们在混乱经历和教育成就方面的差异似乎有着相同的根源。我们的研究表明，改变孩子对混乱的体验不太可能有助于提高他们的教育成就。

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引用次数: 0

Beyond linear regression: Statistically modeling aptitude-treatment interactions and the differential effectiveness of educational interventions 超越线性回归：统计建模能力-处理的相互作用和教育干预的差异效果

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-17 DOI: 10.1016/j.lindif.2025.102812

Peter A. Edelsbrunner , Leonard Tetzlaff , Katharina M. Bach , Denis Dumas , Sarah I. Hofer , Carmen Köhler , Zoya Kozlova , Julia Moeller , Frank Reinhold , Garrett J. Roberts , Marie-Ann Sengewald , Sarah Bichler

Research on aptitude-treatment interactions and the differential effectiveness of educational interventions faces statistical challenges that may contribute to sparse findings and unclear replicability. These challenges include the presence of nonlinear-, floor-, or ceiling effects, underpowered samples, and the multivariate nature of learner aptitudes. Linear regression, which prevails as the typical statistical approach in this research area, lacks the flexibility to meet these challenges. As alternatives, we present three statistical approaches: (1) Additive regression models to capture and control nonlinear or floor/ceiling effects, (2) Bayesian multilevel modeling, which can improve statistical power and allows for more complex models, and (3) clustering multivariate constellations of learner aptitudes via latent profile analysis. We demonstrate these three approaches on a motivating dataset from a scientific reasoning training, discussing their relative (dis-)advantages and how these and further models may aid research into differential effectiveness across different research topics and designs.

Educational relevance statement

In educational interventions, researchers and practitioners are often interested in knowing for whom an intervention works best or worst. We present three statistical models that can help examine this question and overcome issues that have long bugged this field. We discuss how these approaches can help research across multiple areas, for example to examine the effects of educational technologies (augmented & virtual reality).

天赋-治疗的相互作用和教育干预的差异效果的研究面临着统计上的挑战，这可能导致研究结果稀少和不明确的可重复性。这些挑战包括非线性、下限或上限效应的存在、动力不足的样本以及学习者能力的多元性质。线性回归作为这一研究领域的典型统计方法，缺乏应对这些挑战的灵活性。作为替代方案，我们提出了三种统计方法：(1)捕获和控制非线性或地板/天花板效应的加性回归模型；(2)贝叶斯多层建模，可以提高统计能力并允许更复杂的模型；(3)通过潜在剖面分析聚类学习者能力的多元星群。我们在一个来自科学推理训练的激励数据集上展示了这三种方法，讨论了它们的相对（缺点）优势，以及这些模型和进一步的模型如何有助于研究不同研究主题和设计的差异有效性。教育相关性声明在教育干预中，研究人员和实践者通常有兴趣知道干预对谁效果最好或最差。我们提出了三个统计模型，可以帮助检查这个问题，并克服长期困扰这个领域的问题。我们将讨论这些方法如何帮助跨多个领域的研究，例如检查教育技术（增强虚拟现实）的影响。

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引用次数: 0

The future of personalized learning with AI 人工智能个性化学习的未来

IF 9 1区心理学 Q1 PSYCHOLOGY, EDUCATIONAL

Learning and Individual Differences

Pub Date : 2025-10-15 DOI: 10.1016/j.lindif.2025.102813

Jan L. Plass, Fabian Froehlich

Personalized learning has the potential to enhance learning outcomes but faces challenges such as problems measuring learning variables, the lack of theoretical guidance for interventions based on these variables, and ethical considerations. The availability of new AI tools, including generative AI based on large language models (LLMs), has resulted in claims that the problem of personalization has been solved. The goals of this paper are to provide a shared, systematic language for describing adaptivity, adaptability and personalization of learning environments; to broaden how we conceptualize personalization; to define the adaptation cycle; and to discuss which aspects of the cycle can be supported by AI. Also discussed are levels of AI integration, and a proposed architecture for AI-based personalized learning environments.

Education relevance

The personalization of learning, i.e., the accommodation of every person's individual needs during the learning process, has been a goal for educators and learning environment designers alike. However, achieving this goal has been a challenge as key aspects of the personalization cycle face challenges, such as the measurement of learning variables and the lack of theoretical guidance for interventions based on these variables. We also need to consider ethical considerations related to unintended consequences of personalization. In this paper, we aim to deepen the discourse about personalized learning by defining key terms, showing how to broaden current conceptualizations of personalization to include affect, motivation, and socio-cultural variables, and providing terminology for the adaptivity cycle that allows for a more conceptual clarity. We then discuss how AI can support personalization of learning, with a focus on applications beyond chatbots. We introduce levels of AI integration in personalization and discuss an architecture that may enable personalization of learning through generative AI.

个性化学习具有提高学习成果的潜力，但也面临着一些挑战，如测量学习变量的问题、缺乏基于这些变量的干预的理论指导以及伦理考虑。新的人工智能工具的可用性，包括基于大型语言模型（llm）的生成人工智能，已经导致个性化问题已经解决的说法。本文的目标是提供一种共享的、系统的语言来描述学习环境的适应性、适应性和个性化；拓宽我们对个性化的概念；定义适应周期；并讨论人工智能可以支持周期的哪些方面。还讨论了人工智能集成的级别，以及基于人工智能的个性化学习环境的拟议架构。教育相关性学习的个性化，即在学习过程中满足每个人的个性化需求，一直是教育工作者和学习环境设计师的目标。然而，实现这一目标一直是一个挑战，因为个性化周期的关键方面面临挑战，例如学习变量的测量和缺乏基于这些变量的干预的理论指导。我们还需要考虑与个性化带来的意外后果相关的伦理问题。在本文中，我们的目标是通过定义关键术语来深化关于个性化学习的论述，展示如何扩大当前个性化的概念化，包括情感、动机和社会文化变量，并提供适应周期的术语，从而使概念更加清晰。然后我们讨论了人工智能如何支持个性化学习，重点是聊天机器人以外的应用。我们介绍了个性化中的人工智能集成级别，并讨论了一种可以通过生成式人工智能实现个性化学习的架构。

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