Educational Psychology Review最新文献

Emotion regulation (ER) represents an important factor in youth’s academic wellbeing even in contexts that are not characterized by outstanding levels of academic stress. Effective ER not only enhances learning and, consequentially, improves youths’ academic achievement, but can also serve as a protective factor against academic burnout. The relationship between ER and academic burnout is complex and varies across studies. This meta-analysis examines the connection between ER strategies and student burnout, considering a series of influencing factors. Data analysis involved a random effects meta-analytic approach, assessing heterogeneity and employing multiple methods to address publication bias, along with meta-regression for continuous moderating variables (quality, female percentage and mean age) and subgroup analyses for categorical moderating variables (sample grade level). According to our findings, adaptive ER strategies are negatively associated with overall burnout scores, whereas ER difficulties are positively associated with burnout and its dimensions, comprising emotional exhaustion, cynicism, and lack of efficacy. These results suggest the nuanced role of ER in psychopathology and well-being. We also identified moderating factors such as mean age, grade level and gender composition of the sample in shaping these associations. This study highlights the need for the expansion of the body of literature concerning ER and academic burnout, that would allow for particularized analyses, along with context-specific ER research and consistent measurement approaches in understanding academic burnout. Despite methodological limitations, our findings contribute to a deeper understanding of ER's intricate relationship with student burnout, guiding future research in this field.

The present study was designed to understand and optimize self-assessment accuracy in cognitive skill acquisition through example-based learning. We focused on the initial problem-solving phase, which follows after studying worked examples. At the end of this phase, it is important that learners are aware whether they have already understood the solution procedure. In Experiment 1, we tested whether self-assessment accuracy depended on whether learners were prompted to infer their self-assessments from explanation-based cues (ability to explain the problems’ solutions) or from performance-based cues (problem-solving performance) and on whether learners were informed about the to-be-monitored cue before or only after the problem-solving phase. We found that performance-based cues resulted in better self-assessment accuracy and that informing learners about the to-be-monitored cue before problem-solving enhanced self-assessment accuracy. In Experiment 2, we again tested whether self-assessment accuracy depended on whether learners were prompted to infer their self-assessments from explanation- or performance-based cues. We furthermore varied whether learners received instruction on criteria for interpreting the cues and whether learners were prompted to self-explain during problem-solving. When learners received no further instructional support, like in Experiment 1, performance-based cues yielded better self-assessment accuracy. Only when learners who were prompted to infer their self-assessments from explanation-based cues received both cue criteria instruction and prompts to engage in self-explaining during problem-solving did they show similar self-assessment accuracy as learners who utilized performance-based cues. Overall, we conclude that it is more efficient to prompt learners to monitor performance-based rather than explanation-based cues in the initial problem-solving phase.

In current research, emotions in language use situations are often examined only at their starting and ending points, akin to observing the beginning and end of a wave, while neglecting their complex fluctuations in between. To fully comprehend the dynamics of emotions in language use situations, it is essential to delve into their intricate unfolding throughout their progression. This is particularly critical in the context of Second Language Acquisition (SLA), where emotional dynamics can significantly influence learning outcomes and proficiency. Drawing on existing empirical research and theories, we propose a novel interpretation rooted in complex dynamic systems theory (CDST) to elucidate the dynamic nature of emotions in language use situations. Furthermore, we suggest methodologies for capturing the complete dynamics of emotional and language behaviours, including an analysis of their dynamic interrelationships. By embracing a dynamic perspective, we can advance our understanding of interplay between emotions and language behaviours from epistemological theory to methodology and analysis, paving the way for future research in this field.

In this article, I provide the first publication thoroughly detailing how the theoretical foundation for the self-regulated strategy development (SRSD) instructional model was developed. I explain the development of the theoretical base for the SRSD model of instruction and the initial focus on writing instruction. I detail the reasoning and research that provided the base for the theory underlying the SRSD model of instruction. The theoretical base relies, in part, on both theoretical integration and triangulation; I define and provide examples of each process. I address the role of multiple theories, early studies, and iterations that led to the current SRSD instructional model. The tenets of the theoretical base, theoretical principle, and four initial and foundational research questions for the SRSD instructional model are detailed. Research regarding the four initial research questions is summarized, demonstrating the tenets and theoretical principle behind the SRSD model of instruction are valid and meaningful. A sizeable body of research across multiple countries indicates moderate to large effect sizes across many outcomes of SRSD instruction in writing and other complex learning areas. Finally, I address both directions for future research and significant challenges in scaling up SRSD instruction, including paradigm wars and other barriers.

Unnoticed by most, some technology corporations have changed their terms of service to allow user data to be transferred to clouds and even to be used to train artificial intelligence systems. As a result of these developments, remote data collection may in many cases become impossible to be conducted anonymously. Researchers need to react by reconsidering their mode of data collection, raising awareness, and expanding informed consent to ensure information sovereignty. Considerations for data sharing are discussed.

Recent advances in genomics make it possible to predict individual differences in education from polygenic scores that are person-specific aggregates of inherited DNA differences. Here, we systematically reviewed and meta-analyzed the strength of these DNA-based predictions for educational attainment (e.g., years spent in full-time education) and educational achievement (e.g., school grades). For educational attainment (k = 20, n = 16, N_total = 314,757), a multilevel meta-analysis showed an association with polygenic scores of ρ = .27 (95% CI from .22 to .32). For educational achievement (k = 19, n = 10, N_total = 83,788), the association was ρ = .24 (95% CI from .18 to .30). Eurocentric biases were evident with only 15% of estimates being reported in samples of non-European ancestry. After accounting for sample ancestry, age at assessment, and education measure, the meta-analytic estimates increased to ρ = .29 (95% CI from .24 to .33) for educational attainment and ρ = .50 (95% CI from .39 to .61) for educational achievement, indicative of large effect sizes. All meta-analytic estimates were associated with significant heterogeneity. Our findings suggest that DNA-based predictions of education are sizeable but vary across samples and studies. We outline three steps to safeguard potential applications of polygenic score predictions in education to maximize their benefits for personalizing learning, while minimizing the bioethical risks of perpetuating social, cultural, and economic inequalities.

Many recent studies support the idea that creativity is partially or totally “domain-general.” Certain individuals may exhibit greater creativity than the average, whatever the domain. More precisely, certain general factors (e.g., genetic factors, creative personality) could significantly impact creativity. This systematic review aims to evaluate this latter assertion. All empirical papers using at least two creative performance tasks in two domains were selected (n = 36). Results show that some participants succeed in creative tasks in several different domains, but only in experiments where specific prior knowledge is not controlled and tasks are artificial. Furthermore, certain studies conflate the function, which is domain-general, with its functioning, which may not necessarily be domain-general. For these reasons, the results appear less robust (no control for confounding factors) and less representative (creative tasks are not academically, socially, or professionally realistic). Therefore, it seems premature to recommend the integration of general creative skills into school or training programs, as well as the selection of students or employees with a presumed “creative profile.”

Socioeconomic status (SES) has been widely argued to be an important predictor of students’ learning outcomes; the corollary is that low-SES students face immense challenges in their learning. The present study employed an umbrella review, comprising a thematic review and second-order meta-analysis, of 48 reviews relating SES to student learning to critically examine this argument. Results elucidate three sets of insights on the relationship between SES and student learning. First, researchers need to expand the conceptualization of SES from being an objective, individual attribute measuring hierarchical differences in access to resources to a more comprehensive construct incorporating (a) objective and subjective evaluations of (b) individual and collective SES attributes; (c) emphasizing students’ mobilization of capital; and (d) adopting a more diverse, asset-based perspective of SES. Second, the umbrella review provides a nuanced evaluation of the influence of SES in student learning. It yields a mean SES effect size of r = .22 (deemed as large with reference to educational benchmarks, expected students’ achievement gains, and percentile points in achievement tests) but SES effects may be confounded with other demographics. Third, SES needs to be examined from an ecological perspective, given the different mediating and moderating processes pertaining to SES in student learning. The results imply that policies for improving low-SES students’ learning outcomes should comprehensively address multiple factors influencing student learning and avoid a one-size-fits-all approach, enhance disadvantaged students’ access to educational opportunities and resources, affirm the strengths that disadvantaged students bring to their learning, and address structural and systemic inequalities related to poverty, classism, and segregation.

Both achievement goals and achievement emotions have empirically been found to be important within educational contexts. This meta-analysis examined the interrelations among six achievement goals and fifteen achievement emotions and various moderators by analyzing 2,644 effect sizes from 355 studies with 155,208 participants. The findings revealed interrelations among achievement goals and achievement emotions that mainly corroborate theoretical assumptions. Mastery and performance goals showed associations with activity and outcome emotions. The results for work-avoidance goals confirmed the assumption that engaging in work avoidance is particularly related to negative activity emotions. Relational goals are positively linked to positive affect and enjoyment. Facets of mastery goals were identified as relevant moderators of the interrelations among achievement goals and emotions, in contrast to population. The results highlight the relevance of considering the interplay among achievement goals and achievement emotions at a specific level, as opposed to considering only the affective level, as well as differentiating between activity and outcome emotions to better understand their relationships with achievement goals.

Accurate monitoring of performance in problem-solving tasks is an important prerequisite for students’ future academic success. A wide variety of interventions aiming to enhance students’ monitoring accuracy have been developed, but their effectiveness is not apparent from the individual studies in which they have been examined. This meta-analysis classified these interventions in terms of how they target students’ monitoring and investigated their relative effectiveness to improve monitoring accuracy in problem-solving tasks. Findings across the 35 included studies indicated that all interventions combined have a small positive effect on students’ monitoring accuracy (g = 0.25). Intervention type moderated the findings. Interventions on the whole task, metacognitive knowledge, and external standards improved monitoring accuracy. On the other hand, interventions targeting the timing of metacognitive judgment negatively impacted monitoring accuracy and significantly differed from all other interventions. Exploratory moderator analyses of study features indicated that secondary school students benefited least from the interventions compared to primary school students and adults, laboratory studies showed larger effects than classroom studies, and interventions were more effective for retrospective confidence judgments than for judgments of learning. For educational practice, interventions targeting the whole task, metacognitive knowledge, and external standards are recommended, while reconsideration and possibly discontinuation of timing interventions is needed.