Educational Psychology Review最新文献

Recent studies suggest that making judgments of learning (JOLs)—self-assessment of current learning status—may not merely be a neutral cognitive process, but can directly improve learning through what is called ‘JOL reactivity’. This study investigated whether making JOLs can facilitate the learning of previously studied materials (backward effect) and newly studied materials (forward effect) in inductive learning. We also examined how this effect varies depending on whether a JOL is accompanied by a retrieval attempt. Across three experiments, participants learned about various butterfly species presented in two sections (Sections A and B). Some participants made JOLs between Section A and Section B, while others did not, and then all participants took a final transfer test for both sections. In Experiment 1, merely making JOLs did not facilitate learning compared to restudy control, regardless of whether JOLs afforded covert retrieval (target-absent JOL) or not (target-present JOL). However, in Experiment 2, when participants made JOLs combined with overt retrieval prompts (retrieval practice + JOL), they outperformed the other groups in the final transfer test of Section B, showing the forward effect. Experiment 3 further revealed that the act of making JOLs combined with overt retrieval practice was as effective as (but not more than) retrieval practice without JOLs in promoting new learning. Our findings indicate that conventional forms of JOLs do not appear to enhance inductive learning; rather, they underscore the critical role of retrieval in facilitating inductive learning.

Practice testing (i.e., practice retrieval) has been established as an effective learning strategy. Uncovering potential factors influencing self-testing usage is a prerequisite to promote its practical use. The present study reports five experiments exploring whether test anxiety (TA) and test stake (1) affect self-testing usage (Experiments 1–5) and (2) influence learning performance through their negative effects on self-testing usage (Experiments 1, 4, and 5). Experiment 1 analyzed data from 459 high school students collected via a survey and found both that TA negatively predicted students’ daily use of self-testing and that self-testing usage mediated the negative association between TA and academic performance. The negative association between TA and self-testing usage was further replicated in a laboratory experiment (Experiment 2). Another quasi-experiment (Experiment 3) showed that students were less likely to test themselves when preparing for a high-stake than a low-stake exam. Experiment 4 replicated this finding and additionally demonstrated that a high-stake test led to poorer learning via its negative influence on self-testing usage. Experiment 5 demonstrated that a high-stake test provoked high state anxiety, which then induced avoidance of self-testing and ultimately impaired learning. Overall, these findings demonstrate a negative effect of TA on self-testing usage, in turn leading to poor learning. Practical implications are discussed.

Generative AIs have been embraced by learners wishing to offload (parts of) complex tasks. However, recent research suggests that AI users are at risk of failing to correctly monitor the extent of their own contribution when being assisted by an AI. This difficulty in keeping track of the division of labor has been shown to result in placebo and ghostwriter effects. In case of the AI-based placebo effect, users overestimate their ability while or after being assisted by an AI. The ghostwriter effect occurs when AI users do not disclose their AI use despite being aware of the contribution made by an AI. These two troubling effects are discussed in the context of the conflict between cognitive externalization and anthropomorphization. While people tend to offload cognitive load into their environment, they also often perceive technology as human-like. However, despite the natural conversations that can be had with current AIs, the desire to attribute human-like qualities that would require the acknowledgment of AI contributions appears to be lacking. Implications and suggestions on how to improve AI use, for example, by employing embodied AI agents, are discussed.

The paradoxical effects of contextual interference (CI) assume that high CI practices hinder performances during the acquisition phase of learning, while providing more permanent enhancement during the retention phase. This meta-analysis evaluates the possible generalizability of the CI phenomenon in physical education (PE) and sports contexts, with regard to the acute and relatively permanent gains in performance outcomes. A total of 933 records from five electronic databases were screened using the PICOS criteria, of which 36 studies were selected. Outcomes evaluating the performance changes (Δ) from pre-post, post-retention, and pre-retention tests were included. Out of 183 overall pooled outcomes, Δ in only 37 performance outcomes (20%) agreed with the paradoxical CI effects on the acquisition or the relatively permanent gains. No statistically significant overall difference was detected for “Δ pre-post” between low (blocked) (28.9 ± 59.5%) and high (random/serial) (27.9 ± 52.8%) CI (effect size (ES) = 0.1, p = 0.35). An overall significant difference (p = 0.001) in favor of high CI practice was detected in “Δ post-retention.” However, this difference was not large enough (ES = − 0.35) to produce an overall greater long-term gain following high (24.56 ± 4.4%), compared to low (21.9 ± 9.8%) CI (ES = − 0.13, p = 0.18). Out of 10 tested variables, only the age significantly moderated both CI effects (p < 0.0001 for both Δ pre-post and Δ pre-retention) and the female proportion significantly moderated only the first CI effect (p = 0.009 for Δ pre-post). These findings found very limited evidence supporting the recommendation to employ high CI practices to gain a longer-term performance advantage, calling into question the generalization of the CI model to PE and sports practices. High-quality follow-up research evaluating alternative motor-learning models are therefore needed.

The phenomenon of statistics anxiety, prevalent particularly among students engaged in non-mathematical disciplines such as the social sciences, has been linked to a multitude of detrimental outcomes. Over time, several instruments have been developed to measure this construct; however, a comprehensive analysis of these instruments and an adequate evaluation of their psychometric properties have been conspicuously absent. In an attempt to bridge this gap, we undertook a systematic review in accordance with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines using PsycINFO, Scopus, Web of Science, MEDLINE, and PubMed. Our focus was on studies that were published in peer-reviewed English journals and reported a self-report measure of statistics anxiety. These included both original developments and further validations. We employed Skinner’s three-stage framework to assess the methodological quality of the instruments that were retrieved. Out of the 225 results that our search yielded, a mere 28 satisfied the inclusion criteria. The resulting papers reported on the psychometric properties of eight scales. The identified measures undoubtedly provide the potential of capturing some of the key features of the construct. However, our analyses unveiled certain psychometric limitations. Consequently, we advise researchers to either use the most psychometrically robust measures or conduct additional evaluations to ensure the accuracy of their results.

Research in cognitive load theory is increasingly recognizing the importance of motivational influences on students’ (willingness to invest) mental effort, in particular in the context of self-regulated learning. Consequently, next to addressing effects of instructional conditions and contexts on groups of learners, there is a need to start investigating individual differences in motivational variables. We propose here that the biopsychosocial model of challenge and threat may offer a useful model to study the motivational antecedents of (anticipated) mental effort. We also report four experiments as initial tests of these ideas, exploring how feedback valence affects students’ challenge/threat experiences, self-efficacy, and mental effort investment. The results showed that negative feedback leads participants to expect that they will have to invest significantly more effort in future problems than positive feedback (Experiments 1, 2, and 3) or no feedback (Experiment 3). Had we not considered the motivational variables in investigating the effect of feedback conditions on effort investment, we would not have known that this effect was fully mediated and thus explained by participants’ feelings of self-efficacy (Experiments 1/2) and threat (Experiment 1). We would also have concluded that feedback does not affect the willingness to invest effort in future problems (all four experiments), whereas actually, there were significant indirect effects of feedback on willingness to invest effort via challenge (in Experiments 1/2) and threat (in all experiments). Thus, our findings demonstrate the added value of considering challenge and threat motivational states to explain individual differences in effort investment.

Multi-wave-cross-lagged-panel models (CLPMs) of directional ordering are a focus of much controversy in educational psychology and more generally. Extending traditional analyses, methodologists have recently argued for including random intercepts and lag2 effects between non-adjacent waves and giving more attention to controlling covariates. However, the related issues of appropriate time intervals between waves (lag1 intervals across waves) and the possibility of contemporaneous (lag0) effects within each wave are largely unresolved. Although philosophers, theologians, and scientists widely debate sequential (lagged) and simultaneous (lag0) theories of causality, CLPM researchers have mostly ignored contemporaneous effects, arguing causes must precede effects. In a substantive-methodological synergy, we integrated these issues and designed new structural equation models to reanalyze one of the strongest CLPM studies of academic self-concept (ASC) and achievement (five annuals of mathematics data; 3527 secondary school students). A taxonomy of models incorporating various combinations of lag0, lag1, and lag2 effects, random intercepts, and covariates consistently supported a priori reciprocal effect model (REM) predictions—medium or large reciprocal effects of ASC and achievement on each other. Consistent with self-concept theory, effects of ASC on achievement evolved over time (lag1, not lag0 effects), whereas effects of achievement on ASC effects were more contemporaneous (lag0, not lag1 effects). We argue that lag0 effects reflect proximal events occurring subsequent to the previous data wave, suggesting the need for shorter intervals but also leaving open the possibility of contemporaneous effects that are truly instantaneous. We discuss limitations and future directions but also note the broad applicability of our statistical models.

The transition to university is a time of great change and adjustment. The challenges of university life can lead to numerous negative consequences for the students. Despite the importance of successful transition for both the student and the university, the current body of literature comprises methodological inconsistencies and disparate analytical goals that make it difficult to identify the most salient and effective factors that help predict transition success. This paper presents a systematic review of research linking personal level risk and protective factors to the outcome of academic achievement among students making the transition to university. This is part of a larger review, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Synthesis Without Meta-analysis (SWiM) guidelines, preregistered on the International Prospective Register of Systematic Reviews (PROSPERO, CRD42022330515), searching PsychInfo, Web of Science, and ERIC databases. Records were included if they studied ‘traditional’ first year students transitioning to university and were longitudinal in design and excluded if they looked at specific subgroups of students (e.g. international students). The search yielded 27 articles that were eligible, highlighting a broad range of salient factors ranging from personality traits to procrastination and perfectionism. The findings are discussed in relation to moving the research forward towards an intervention to enhance the probability of successful student transition to university.

Self-regulated learning depends on task difficulty and on learners’ resources and cognitive load, as described by an inverted U-shaped relationship in Seufert’s (2018) model: for easy tasks, resources are high and load is low, so there is no need to regulate, whereas for difficult tasks, load is too high and resources are too low to regulate. Only at moderate task difficulty do learners regulate, as resources and load are in equilibrium. The purpose of this study is to validate this model, i.e., the inverted U-shaped relationship between task difficulty and self-regulatory activities, as well as learner resources and cognitive load as mediators. In the within-subject study, 67 participants reported their cognitive and metacognitive strategy use for four exams of varying difficulty. For each exam task difficulty, cognitive load, and available resources (such as prior knowledge, interest, etc.) were assessed. Multilevel analysis revealed an inverted U-shaped relationship between task difficulty and the use of cognitive strategies. For metacognitive strategies, only a linear relationship was found. Increasing cognitive load mediated these relationship patterns. For learner resources we found a competitive mediation, indicating that further mediators could be relevant. In future investigations a broader range of task difficulty should be examined.

We conducted a systematic review of research involving K-12 students that examined associations among individual differences factors (e.g., working memory) and intertextual integration. We identified 25 studies published in 23 peer-reviewed journal articles and two dissertations/theses. These examined a wide range of individual difference factors, which we organized into four categories: (a) language and literacy, (b) cognition and metacognition, (c) knowledge and beliefs, and (d) motivation, emotion, and personality. We found large variation in the participants, tasks, and document types, and little systematic replication across studies. Nonetheless, results generally showed that variation in literacy, cognition, metacognition, knowledge, beliefs, and motivation are positively and moderately associated with intertextual integration. We discuss the limitations of this work and offer four recommendations for future research.