Abstract In this meta-analysis, we examined the effects on students’ motivation of student-centered, problem-driven learning methods compared to teacher-centered/lecture-based learning. Specifically, we considered problem-based (PBL), project-based (PjBL), and case-based learning (CBL). We viewed motivation as a multifaceted construct consisting of students’ beliefs (competence and control beliefs), perceptions of task value (interest and importance), and reasons for engaging in tasks (intrinsic or extrinsic). In addition, we included students’ attitudes toward school subjects (e.g., science). We included 139 subsamples from the 132 included reports (83 PBL, 37 PjBL, and 19 CBL subsamples). Overall, PBL, PjBL, and CBL had a small to moderate, heterogeneous positive effect ( d = 0.498) on motivation. Moderator analyses revealed that larger effect sizes were found for students’ beliefs, values, and attitudes compared to students’ reasons for studying. No differences were found between the three instructional methods on motivation. However, effect sizes were larger when problem-driven learning was applied in a single course (when compared to a curriculum-level approach). Larger effects were also found in some academic domains (i.e., healthcare and STEM) than in others. While the impact of problem-driven learning on motivation is generally positive, the intricate interplay of factors such as academic domain and implementation level underscores the need for a nuanced approach to leveraging these instructional methods effectively with regard to increasing student motivation. 

The Effects of Problem-Based, Project-Based, and Case-Based Learning on Students’ Motivation: a Meta-Analysis

In the context of global integration and increasing reliance on Artificial Intelligence (AI) in education, evaluating the AI literacy of pre-service teachers is crucial. As future architects of educational systems, pre-service teachers must not only possess pedagogical expertise but also a strong foundation in AI literacy. This quantitative study examines AI literacy among 529 pre-service teachers in a Nigerian university, utilizing structural equation modeling (SEM) for comprehensive analysis. The research explores various dimensions of AI literacy, revealing that a profound understanding of AI significantly predicts positive outcomes in AI use, detection, ethics, creation, and problem-solving. However, no correlation exists between AI knowledge and emotion regulation or the assumption that active AI use enhances AI detection capabilities. The study identifies a trade-off between AI application and creation, emphasizing the ethical considerations intertwined with emotional and persuasive facets of AI use. It also supports the link between AI creation and problem-solving, emphasizing the foundational role of AI knowledge in shaping diverse aspects of AI literacy among pre-service teachers. The findings offer valuable insights for educators, administrators, policymakers, and researchers aiming to enhance AI literacy in pre-service teacher education programs. 

pdf/examining-artificial-intelligence-literacy-among-pre-service-14ets55gs9.pdf

Examining artificial intelligence literacy among pre-service teachers for future classrooms

Abstract The Programme for International Student Assessment (PISA) has become the world’s largest comparative assessment of academic achievement. While hundreds of studies have examined the factors predicting student achievement in PISA, a comprehensive overview of the main predictors has yet to be completed. To address this gap, we conducted a systematic literature review of factors predicting mathematics performance in PISA. Guided by Bronfenbrenner’s ecological model of human development, we synthesized the findings of 156 peer reviewed articles. The analysis identified 135 factors that fall into five broad categories: individual student, household context, school community, education systems and macro society. The analysis uncovered seven factors that are consistently associated with math achievement in PISA. Student grade level and overall family SES (socio-economic status) are consistently positively associated with math achievement while five factors are consistently negatively associated with math achievement: student absenteeism and lack of punctuality, school repeating and dropout rate, school prevalence of students’ misbehavior, shortage of teachers and general staff, and student-centered instruction. Fourteen factors tend to be positively or negatively associated with math achievement. The explanatory power of many other factors, however, remain mixed. Explanations for this result include methodological differences, complex interactions across variables, and underlying patterns related to national-cultural context or other meso or macro-level variables. Implications for policy and research are discussed. 

/pdf/factors-predicting-mathematics-achievement-in-pisa-a-1sxb5jav.pdf

Factors predicting mathematics achievement in PISA: a systematic review

Abstract This study examines the relationship between student perceptions and their intention to use generative artificial intelligence (GenAI) in higher education. With a sample of 405 students participating in the study, their knowledge, perceived value, and perceived cost of using the technology were measured by an Expectancy-Value Theory (EVT) instrument. The scales were first validated and the correlations between the different components were subsequently estimated. The results indicate a strong positive correlation between perceived value and intention to use generative AI, and a weak negative correlation between perceived cost and intention to use. As we continue to explore the implications of GenAI in education and other domains, it is crucial to carefully consider the potential long-term consequences and the ethical dilemmas that may arise from widespread adoption. 

An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI

Abstract Strengthening social and emotional skills can be particularly relevant concerning the emerging skills need as it yields benefits for individuals’ successful development. A growing body of research suggests that social and emotional competences are associated with well-being and positive life and academic outcomes. Despite the notable benefits of social and emotional skills, assessment tools are still scarce or target specific risk and problematic behaviours. This systematic review seeks to address this gap and identify instruments measuring social and emotional skills for students in elementary through secondary education. This review also aims to describe the study characteristics and key features of the identified instruments and to assess the extent to which the instruments comprehensively evaluate the five Collaborative for Academic, Social, and Emotional Learning (CASEL) areas of competence. A systematic search of the literature was carried out in Scopus, Web of Science and ERIC databases. This review resulted in the identification of 25 unique assessments over a 20-year period, reported on the basis of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Our results suggest a rapid growth and notable advancements of social and emotional skills assessment systems and demonstrate the direct influence of the CASEL framework. Our results also show that a combination of multi-method and multi-informant assessment should be employed to effectively assess social and emotional skills. Thus, this paper contributes to support school-based practitioners and psychologists in their efforts to lead social and emotional skills instruction and assessment in routine educational practice. 

/pdf/a-systematic-review-of-instruments-measuring-social-and-11ypt7ob.pdf

A Systematic Review of Instruments Measuring Social and Emotional Skills in School-Aged Children and Adolescents

This study investigated the effects of social-emotional competency on pupils' academic achievement, academic emotions and attitudes, and interpersonal relationships. Participants were 7106 fourth-grade and fifth-grade students in western China. The results were: (1) social-emotional competency positively predicted pupils' academic achievement (including reading, mathematics, and science); (2) social-emotional competency predicted pupils' academic emotions and attitudes, including learning anxiety and interest, and academic emotions and attitudes played a mediating role in the relation between social-emotional competency and academic achievement; and (3) social-emotional competency positively predicted pupils' interpersonal relationships, including peer relationships and teacher-student relationships, and interpersonal relationships played a mediating role in the relation between social-emotional competency and academic achievement. These findings highlighted the importance of social-emotional competency to child development in western China, where many children might lack their parents' company.

/pdf/the-effect-of-social-emotional-competency-on-child-1zdbooehcn.pdf

The Effect of Social-Emotional Competency on Child Development in Western China

Abstract Despite the importance of artificial intelligence (AI) for university students to thrive in the future workplace, few studies have been conducted to assess and foster their intentions to learn AI. Guided by the situated expectancy–value theory, this study adopted both variable- and person-centered approaches to explore the role of supportive environments and expectancy–value beliefs in fostering university students’ intentions to learn AI. The data were drawn from 494 university students. In Study 1, the variable-centered approach of structural equation modeling showed the critical role of supportive environments and expectancy–value beliefs in promoting students’ intentions to learn AI. In Study 2, the person-centered approach of latent profile analysis identified three subgroups of students based on their levels of supportive environments and expectancy–value beliefs. Consistent with Study 1, students who perceived more supportive environments and higher levels of expectancy–value beliefs had stronger intentions to learn AI. We also documented the influence of study of field, gender, and year level on students' perceptions of supportive environments, expectancy-value beliefs and intentions to learn AI. The implications of these findings in improving students’ intentions to learn AI are discussed. 

University students’ intentions to learn artificial intelligence: the roles of supportive environments and expectancy–value beliefs

Why do East Asian students do so well in mathematics? A machine learning study

ABSTRACT Disciplinary climate and opportunity to learn (OTL) are considered as effectiveness-enhancing factors that can improve mathematics achievement. In this study, we investigated whether the school-level aggregation of student-reported OTL could yield reliable and valid measures, and then explored the relationships among disciplinary climate, OTL, and mathematics achievement at both school and student levels. Doubly latent multilevel structural equation modeling was adopted to analyze data from 63 countries/economies measured in the Programme for International Student Assessment (PISA) 2012. Three key findings emerged: (1) both disciplinary climate and OTL were reliable constructs when used at the school level, (2) disciplinary climate and OTL had positive effects on achievement at the school level, and OTL mediated the influence of disciplinary climate on achievement, and (3) OTL was positively associated with student achievement at the student level. Methodological and practical implications were discussed.

Disciplinary climate, opportunity to learn, and mathematics achievement: an analysis using doubly latent multilevel structural equation modeling

Motivated Strategies for Learning Questionnaire (MSLQ): Adaptation, validation, and development of a short form in the Chinese context for mathematics

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