Educational Psychology Review最新文献

Over a dozen rigorous randomized-controlled trials show that recognizing worries about belonging in a new school as normal and as improving with time can help students stay engaged, build relationships, and succeed. Such “social-belonging” interventions can help students take advantage of opportunities available to them to develop their belonging in college—yet what is the institutional role? Drawing on past literature, and reporting novel data from the College Transition Collaborative’s massive trial of the social-belonging intervention (N = 15,143 control-condition students in 374 “local-identity” groups across 22 representative colleges and universities; Walton et al., 2023), we explore who gets to belong in college and what institutional leaders can do to expand these opportunities. First, we find that opportunities for belonging (i.e., “belonging affordances”) vary widely, both across institutions and systematically across groups. Notably, Black, Asian, and first-generation college student groups are each less likely than other groups to have minimally adequate opportunities for belonging. Second, all institutions are serving some student groups well, but all can improve: none provides adequate belonging affordances for all groups. Third, four classes of institutional factors predict belonging affordances at the identity-group level: (1) greater in-group representation, (2) more inclusive cultures, (3) greater opportunities for strong relationships, and (4) greater opportunities for productive learning. We conclude by discussing how institutions can learn for whom they are creating opportunities for belonging and for whom they are not, and how institutions can expand opportunities for belonging for groups that are not yet well served.

Recent research underscores the importance of inquiry learning for effective science education. Inquiry learning involves self-regulated learning (SRL), for example when students conduct investigations. Teachers face challenges in orchestrating and tracking student learning in such instruction; making it hard to adequately support students. Using AI methods such as machine learning (ML), the data that is generated when students interact in technology-enhanced classrooms can be used to track their learning and subsequently to inform teachers so that they can better support student learning. This study implemented digital workbooks in an inquiry-based physics unit, collecting cognitive, metacognitive, and affective data from 214 students. Using ML methods, an early warning system was developed to predict students’ learning outcomes. Explainable ML methods were used to unpack these predictions and analyses were conducted for potential biases. Results indicate that an integration of cognitive, metacognitive, and affective data can predict students’ productivity with an accuracy ranging from 60 to 100% as the unit progresses. Initially, affective and metacognitive variables dominate predictions, with cognitive variables becoming more significant later. Using only affective and metacognitive data, predictive accuracies ranged from 60 to 80% throughout. Bias was found to be highly dependent on the ML methods being used. The study highlights the potential of digital student workbooks to support SRL in inquiry-based science education, guiding future research and development to enhance instructional feedback and teacher insights into student engagement. Further, the study sheds new light on the data needed and the methodological challenges when using ML methods to investigate SRL processes in classrooms.

Increasing the playback speed of video lectures is popular amongst students as a time saving strategy, but does this negatively impact test performance? Here, we conducted a meta-analysis to examine the effect of increasing video lecture playback speed on content test performance. A meta-regression with robust variance estimation was used to aggregate data from 110 effect sizes, stemming from 24 studies of learning from lecture videos. The results demonstrated that increasing the playback speed of lectures can negatively impact content test performance, but this cost is small (and often non-significant) for speeds 1.5 x and slower. In addition, we found no evidence of moderation of this cost by a number of theoretically important variables (e.g., test type, lecture duration). These results contribute important insights into a popular study strategy and one that is likely to be a mainstay in educational settings for years to come.

Cognitive load theory’s incorporation of evolutionary perspectives suggests biologically primary knowledge, acquired through evolutionary processes, can support students in learning biologically secondary knowledge, the focus of typical educational curricula. Touch-based interactions using the hands are likely to be biologically primary. The present study investigates the effects of tracing and pointing actions using the index finger on the surface of lesson materials when learning about the human circulatory system. Although there are a few studies which have assessed the effects of tracing and pointing when learning from multimedia (text and diagrams), these studies have mostly used Western languages (English and German) as the medium of instruction. This project replicates and extends prior research by investigating whether tracing and pointing can enhance learning when the language of instruction is Chinese. A total of 84 native Chinese-speaking adult participants were randomly assigned to conditions in which they either traced and pointed or did not trace or point while learning about the structure and function of the human heart from paper-based materials. Replicating previous results with Western samples, participants who traced and pointed in the learning phase outperformed participants who did not trace and point on a knowledge test of comprehension, while also reporting lower extraneous load. Implications and recommendations for future research are discussed.

Failure can be an effective tool for learning, but it comes with negative consequences. Educators and learners should practice strategies that leverage the benefits of failure while managing its negative consequences on learners’ motivation and persistence. Towards that goal, this paper examines the biological effects of failure on learning to (1) explain how failure primes the brain for learning and (2) propose behavioral strategies for coping with the negative consequences, focusing on postsecondary students. This conceptual literature review article draws upon neuroscience literature to explain biological mechanisms related to failure and education literature to explore connections to learning theory and environments. The paper is organized into two major sections: (1) the benefits of failure and (2) tools to deal with its negative effects. Within each section, the paper describes related neurochemicals and behavioral strategies to affect them that could be explored in educational settings. By understanding these biological effects, we can better design learning environments and support students through failure. Each section of the paper also describes non-invasive research tools that could be used to study the effects of interventions that aim to improve students’ experience of failure in education.

The persistence of learning styles as a concept in educational discourse and research is paradoxical, given the overwhelming evidence discrediting the matching hypothesis, the notion that aligning teaching methods with students’ preferred learning styles enhances achievement. This paper examines the resurgence of learning styles across meta-analyses and proposes an explanation for their enduring appeal. Drawing on 17 meta-analyses, we distinguish between studies testing the matching hypothesis (effect size d = .04) and correlational studies (average correlation r = .24), revealing that the latter often conflates learning styles with learning strategies. Much of the research is flawed, and the argument is that there needs to be a shift away from matching learning styles toward teaching students adaptable and effective learning strategies that align more closely with task complexity and learning goals.

Marsh et al. (Educational Psychology Review, 36(2), 53, 2024) recently reported associations between academic achievement and self-concept (i.e., self-perceived academic competence). Marsh et al. claimed that their analyses supported a reciprocal effects model, according to which academic achievement and self-concept reinforce one another. Marsh et al. (Educational Psychology Review, 36(2), 53, 2024) further recommended to test alternative models and juxtapose their results and interpretations. Here, we followed this recommendation and tested different models using data simulated to resemble the data they used. However, contrary to Marsh et al. (Educational Psychology Review, 36(2), 53, 2024), in the present analyses we found contradictory positive, negative, and null effects between within-individual math self-concept and subsequent change in within-individual math achievement and vice versa. This suggests that the findings by Marsh et al. (Educational Psychology Review, 36(2), 53, 2024) may have been spurious and that the reciprocal effects model can be challenged.

Substantial research has explored the connection between children’s spontaneous focusing on numerosity (SFON) and their current and later mathematical achievement. However, the findings have been inconsistent, and no comprehensive investigation has yet been conducted. This meta-analysis examines the relation between SFON and mathematics performance in preschool and school-age students and identifies potential moderators, including SFON measurement methods, SFON scoring methods, number size in SFON tasks, domains of mathematics skills, temporal relations, time lags, and age. Based on 171 effect sizes from 35 independent samples, involving 6604 participants across 29 studies, results revealed a significant positive correlation between SFON and mathematics performance (r = .274), with bidirectionality and longitudinal stability. The correlation remained significant after controlling for working memory, nonverbal intelligence, and inhibition, respectively. Moderation analyses showed stronger correlations with mathematics performance for behavioral-based tasks and scoring methods that consider both accuracy and quantifying acts. SFON tasks involving smaller numbers (less than ten) yielded stronger correlations with mathematics performance. Temporal relations emerged as a significant moderator, with a stronger longitudinal link from mathematics performance to SFON compared to the reverse. Age was another significant moderator, with the link strengthening with age. Interaction effects among moderators were also observed. Theoretical implications of these findings regarding the SFON-mathematics performance relation are discussed, and practical implications for comprehensive assessment of children’s SFON.

This commentary provides a constructive analysis of the meta-analysis by Ammar et al. Educational Psychology Review 36(2), 2024 on the contextual interference (CI) effect in applied settings, published in Educational Psychology Review. The authors are commended for significant advancements over their previous work (Ammar et al. Educational Research Review, 39, 100537, 2023), including addressing methodological limitations and incorporating sensitivity analyses to enhance the robustness of their findings. While their efforts represent a valuable contribution to understanding the CI effect, several areas warrant further refinement. Expanding database selection to include specialized resources in psychology, sports, and kinesiology could reduce the risk of missing relevant studies. Additionally, while pre-registration is not obligatory, its inclusion would enhance validity and transparency and mitigate potential biases. Some methodological inconsistencies, such as the inclusion of studies with differing definitions of CI practice, highlight the need for greater clarity in study classification. Overall, this commentary highlights areas for improvement while recognizing the authors’ contribution to advancing research on the CI effect in applied contexts.

The aim of the current paper is to offer a unique perspective on eye movement analysis in reading research by applying techniques from social network analysis to examine integration processes between sentences during reading. In a first step, we explored how network measures relate to the often-used duration measures in reading research in order to examine whether there is an additional value in using network measures. In a second step, we further explored how differences in network measures are related to text (i.e., topic structure) and reader characteristics (i.e., WMC). Thirty-one participants read three short expository texts. Four network measures at the sentence level were calculated for the three texts: strength, betweenness centrality, harmonic centrality, and local clustering coefficient. Correlations were computed between first-pass reading time and second-pass reading time and the network measures. Network measures were analyzed with (generalized) linear mixed-effects models. The results show that strength is strongly correlated to second-pass reading time. Betweenness, harmonic centrality, and the local clustering coefficient are not related to these often-used duration measures and thus capture aspects of integration processes that cannot be captured with duration measures. The results demonstrated that strength and betweenness centrality are related to reader’s WMC. It was also shown that strength, harmonic centrality, and local clustering coefficient were related to the topic structure of the text. This study demonstrates that a social network approach offers a novel perspective on moment-to-moment integration processes during reading.