Search Results (36)

Preschool teaching is a highly demanding profession that requires constant socio-emotional attunement and the ability to engage in reflective reasoning. Despite the central role of these skills in effective early childhood education, little is known about whether preschool teachers’ socio-affective and cognitive capacities vary as a function of accumulated professional experience. To address this knowledge gap, we compared the performance of 30 professional preschool teachers with a matched control group of 30 non-teachers on tests measuring emotion recognition, active-empathic listening, interpersonal reactivity, and abstract reasoning. We found that preschool teachers were significantly better on all dimensions of active-empathic listening (sensing, processing, and responding) and better in emotional self-regulation than controls. Moreover, years of preschool teaching experience were positively correlated with emotion recognition, improved listening skills, and more deliberate abstract reasoning strategies. Notably, socio-affective competencies were correlated with abstract reasoning performance within the preschool teacher group. According to these results, long-term professional involvement in preschool teaching enhances socio-affective skills and integrates them with higher-order cognitive processes, both of which are essential for responsive teaching, efficient classroom management, and the development of children’s social and cognitive abilities. Full article

This study explores how adolescent learners conceptualize the emotion of love in their first language (Spanish) and in English as a foreign language (EFL), comparing monolingual Spanish speakers and Spanish–Arabic bilinguals. A total of 66 participants (33 per group), all with A2 proficiency in English, completed a semantic fluency task in both Spanish and English, producing as many words as possible in relation to the prompts Amor and Love. The data were analyzed using graph theory to capture the organization of participants’ emotion lexicons. The results show that love is a highly productive and cohesive semantic field, eliciting significantly more responses in L1 than in L2, for both Spanish-only (t = −8.866, p < 0.001) and Spanish–Arabic (W = 101.0, p = 0.001) participants. The differences between the two learner cohorts were not significant in Spanish nor in English. The results from the graph analyses revealed that learners displayed rich and strongly connected networks in Spanish, with learners with a migration origin showing slightly more fragmented networks. In English, both groups performed similarly, with responses probably mediated by L1 translation equivalents and metaphorical associations (e.g., heart, flower, and red). The findings suggest that emotional lexicons are better developed and more efficiently organized in the L1, whereas FL representations are shaped by proficiency, context of learning, and reliance on L1 conceptual structures. This study contributes novel insights into bilingual and heritage learners’ emotional conceptualization and highlights the value of graph analysis for examining the structure of emotion words. Full article

Teacher certification is strongly correlated with student development. Many studies have documented the effect of teacher certification on student achievement. However, there are inconsistent conclusions about this issue. Moreover, few studies have examined the mechanisms by which teacher certification promotes student achievement. To fill these gaps, this paper examines the effect of teacher certification on student achievement and the underlying mechanisms. We analyzed the data from the TALIS 2018 Türkiye teacher data and the PISA 2018 Türkiye student data using path analysis and PROCESS Model 6. It was found that the rise in entry requirements for teacher certification was positively associated with teachers’ sense of efficacy and pedagogical innovation in the Turkish context. It was also indicated that teacher certification was positively associated with student achievement through the serial mediation of teachers’ sense of efficacy and pedagogical innovation. The practical and theoretical implications of this paper were discussed. Full article

Emotional intelligence (EI), specifically the capacity to recognize and understand one’s own emotions and those of others, is pivotal for developing the interpersonal skills that foster effective collaboration. This is especially crucial for developing trust in others, which serves as the necessary foundation for functioning in our increasingly impersonal contemporary society. Although extensive research has been conducted on trust in adults, empirical evidence for children remains limited. Quantifying the extent to which trust exists in young children, whether it differs from trust in adults, and how it changes with age, gender, and various psychological and school culture factors is essential for understanding how educational environments can foster its development. In this article, we analyze trust among almost 3000 fourth-grade children from 135 schools, measured based on behaviors exhibited during a Public Goods Game. The results align with other studies, showing that trust is substantially higher towards the in-group (classmates) than the out-group. A notable gender effect was observed, with boys exhibiting significantly higher levels of trust than girls. Trust was also higher in municipal schools compared to state-subsidized private schools. Personality traits, measured via the Big Five model using the Pictorial Personality Traits Questionnaire for Children (PPTQ-C), also emerged as influential. Specifically, elevated levels of Agreeableness and Conscientiousness predicted increased trust in both in-groups and out-groups. Extraversion and Openness to Experience also played a role, although to a lesser extent. Full article

Metacognition and self-regulated learning (SRL) are widely recognized as key mechanisms for academic achievement and skill development, yet in music education they have rarely been examined through explicit instructional interventions to enable causal testing and effect evaluation. To address this gap, this study followed PRISMA guidelines and conducted a systematic review of 31 studies (including seven for meta-analysis) to identify intervention types and mechanisms, and to quantify their overall effects and moderating factors. Results indicate the following: (1) the intervention ecology is grounded in structured learning support (SLS), frequently combined with strategy teaching (ST) or technology-enhanced interventions (TEI), with full integration concentrated at the university level. (2) The mechanisms operate primarily along four pathways: structure facilitates a “plan–practice–reflection” loop, strategy instruction makes tacit experience explicit, technological feedback provides a third-person perspective, and teacher support stabilizes motivation. (3) The meta-analysis revealed a significant positive medium effect overall. (4) Intervention structure moderated outcomes, though not as a single or stable determinant. (5) Effects followed a U-shaped pattern across educational stages, strongest in secondary school, followed by university, and weaker in preschool and primary. Future research should employ proximal, task-aligned measures, conduct parallel multi-indicator assessments within the same stage, and expand evidence for multi-mechanism integration in primary and secondary school contexts. Experimental designs manipulating levels of SLS are needed to test whether ST + TEI remain effective under low-structure conditions, thereby identifying the minimum structural threshold. Extending samples to informal and professional music learners would further enhance robustness and generalizability. Full article

The widespread application of Generative Artificial Intelligence (Gen-AI) is transforming educational practices and driving pedagogical innovation. While cultivating higher-order thinking (HOT) represents a central educational goal, its achievement remains an ongoing challenge. Current evidence regarding the impact of Gen-AI on HOT is relatively fragmented, lacking systematic integration, particularly in the analysis of moderating variables. To address this gap, a meta-analysis approach was employed, integrating data from 29 experimental and quasi-experimental studies to quantitatively assess the overall impact of Gen-AI on learners’ HOT and to examine potential moderating factors. The analysis revealed that Gen-AI exerts a moderate positive effect on HOT, with the most significant improvement observed in problem-solving abilities, followed by critical thinking, while its effect on creativity is relatively limited. Moderation analyses further indicated that the impact of Gen-AI is significantly influenced by experimental duration and learners’ self-regulated learning (SRL) abilities: effects were strongest when interventions lasted 8–16 weeks, and learners with higher SRL capacities benefited more substantially. Based on the research findings, this study proposed that Gen-AI should be systematically integrated as a targeted instructional tool to foster HOT. Medium- to long-term interventions (8–16 weeks) are recommended to enhance learners’ problem-solving and critical thinking abilities. At the same time, effective approaches should also be explored to promote creative thinking through Gen-AI within existing pedagogical frameworks. Furthermore, individual learner differences should be accounted for by adopting dynamic and personalized scaffolding strategies to foster SRL, thereby maximizing the educational potential of Gen-AI in cultivating innovative talents. Full article

Conversational interactions, rich in both linguistic and vocal cues, provide a natural context for studying these processes. In this work, we propose an explainable multimodal transformer framework that integrates textual semantics (via RoBERTa) and acoustic prosody (via WavLM) to advance emotion understanding. By projecting both modalities into a shared latent space, our model captures the complementary contributions of language and speech to affective communication, achieving an 0.83 accuracy value across five emotion categories. Crucially, we embed explainable AI (XAI) techniques including Integrated Gradients and Occlusion to attribute predictions to specific linguistic tokens and prosodic patterns, thereby aligning computational mechanisms with human cognitive processes of emotion perception. Beyond performance gains, this work demonstrates how multimodal AI systems can support transparent, human-centered emotion recognition. Full article

This study introduces and validates the construct of Teacher Emotional Commitment (CED), understood as the conative–behavioral dimension that characterizes the emotional bond that teachers establish with their students. To this end, two complementary studies were conducted in the Autonomous Community of the Canary Islands (Spain), with the aim of: to empirically isolate the factorial structure of CED and differentiating it from related constructs, such as empathy; to analyze its presence in both active teachers and those in initial training; and to test the theoretical model’s validity by expanding the sample and enlarging the response scale. Study 1 involved 854 practicing teachers and 701 teachers in training, following a validation process that included exploratory and confirmatory factor analysis, as well as item response theory models. The results showed a four-factor structure: loving proactivity, teacher compassion, instructional commitment, and communicative affectivity, with adequate reliability and discriminant validity indices with respect to empathy. Study 2, with an expanded sample of 2096 participants, confirmed the robustness of the model. The findings allow us to consider CED as a psychological competence that can be trained, with relevant implications for improving the educational relationship, student learning, and the emotional well-being of teachers. Full article

This article presents and validates the Metacognitive Knowledge Intervention for Thinking (MKIT)—an educational framework designed to assess and develop domain-general metacognitive knowledge (MK) in children aged 5 to 9. Moving beyond traditional approaches that examine metacognition within isolated subject areas, this research reconceptualizes MK as a transferable learning resource across content domains and developmental stages. Moreover, by employing a stepped-wedge design—a rigorous but rarely used approach in education—the study introduces a methodological advancement. Simultaneously, MK is operationalized through an ecologically valid and developmentally appropriate format, using visually engaging stories, illustrated scenarios, and interactive tasks integrated within classroom routines. These adaptations enabled young learners to engage meaningfully with abstract metacognitive concepts. Therefore, across three interconnected studies (N = 458), the MKIT provided strong psychometric evidence supporting valid inferences about metacognitive knowledge, age-invariant effects, and substantial gains among children with initially low MK levels. In addition, qualitative data indicated MK transfer across contexts. Thus, these findings position MKIT as a scalable tool, supported by multiple strands of validity evidence, that makes metacognitive knowledge teachable across domains—offering a practical approach to strengthening learning, reducing early achievement gaps, and supporting the development of core components of intelligence. Full article

This study comprehensively analyses how AI tools scaffold and share metacognitive processes, thereby facilitating students’ learning in STEM classrooms through a mixed-method research synthesis combining bibliometric analysis and systematic review. Using a convergent parallel mixed-methods design, the study draws on 135 peer-reviewed articles published between 2005 and 2025 to map publication trends, author and journal productivity, keyword patterns, and theoretical frameworks. Data were retrieved from Scopus and Web of Science using structured Boolean searches and analysed using Biblioshiny and VOSviewer. Guided by PRISMA 2020 protocols, 24 studies were selected for in-depth qualitative review. Findings show that while most research remains grounded in human-centred conceptualisations of metacognition, there are emerging indications of posthumanist framings, where AI systems are positioned as co-regulators of learning. Tools like learning analytics, intelligent tutoring systems, and generative AI platforms have shifted the discourse from individual reflection to system-level regulation and distributed cognition. The study is anchored in Flavell’s theory of metacognition, General Systems Theory, and posthumanist perspectives to interpret this evolution. Educational implications highlight the need to reconceptualise pedagogical roles, integrate AI literacy in teacher preparation, and prioritise ethical, reflective AI design. The review provides a structured synthesis of theoretical, empirical, and conceptual trends, offering insights into how human–machine collaboration is reshaping learning by scaffolding and co-regulating students’ metacognitive development in STEM education. Full article

In this preregistered multi-level meta-analysis, we aim to clarify the association of need for cognition (NFC) and typical intellectual engagement (TIE) with intelligence and executive functions. Multi-level models with robust variance estimation were specified and risk of bias was assessed with the adapted Risk of Bias Utilized for Surveys Tool. NFC/TIE was associated with fluid intelligence (Gf; r = 0.18, p < .001, N = 25,367), crystallized intelligence (Gc; r = 0.26, p < .001, N = 14,651), general intelligence (r = 0.23, p < .001, N = 8479), and working memory (WM) capacity (r = 0.15, p < .001, N = 5921). Associations with WM updating (r = 0.08, p = .111, N = 1084), inhibition (r = 0.04, p = .077, N = 2895), and shifting (r = 0.01, p = 0.642, N = 1727) were non-significant. NFC (r = 0.19) was more strongly related to Gf than TIE (r = 0.12; F(1, 12.10) = 5.04, p = .045) whereas TIE (r = 0.35) was more strongly associated with Gc than NFC (r = 0.24; F(1, 13.10) = 10.70, p = .006). Correlations with Gc significantly declined over time (b₁ = −0.006, β₁ = −0.04, p = .010). Results provide strong evidence for small-to-moderate associations of NFC and TIE with Gf, Gc, general intelligence, and WM capacity, and at best small associations with core executive functions. Full article

To meet the growing demands for competency-based and personalized instruction in high school English reading, this study investigates a quantitative approach to modeling learning pathways and progressions. Traditional assessments often fail to capture students’ fine-grained cognitive differences and provide limited guidance for individualized teaching. Based on cognitive diagnostic theory, this study analyzes large-scale empirical data to construct a progression framework reflecting both the sequencing of cognitive skill development and the hierarchical structure of reading abilities. A Q-matrix was calibrated through expert consensus. A hybrid cognitive diagnostic model was used to infer students’ knowledge states, followed by cluster analysis and item response theory to define progression levels, which were mapped to national curriculum standards. The findings reveal that students’ mastery of cognitive attributes follows a stepwise developmental pattern, with dominant learning trajectories. The constructed learning progression aligns well with curriculum-based academic quality levels, while uncovering potential misalignments in the positioning of some skill levels. Students with identical scores also showed significant variation in cognitive structures. The proposed model provides a data-informed foundation for adaptive instruction and offers new tools for personalized learning in English reading comprehension. Full article

The relationship between cognitive abilities and students’ achievement in math is well documented. However, theoretical views on the chronological development of cognitive ability and math success remain controversial. Empirically, mutual effects between these concepts amongst primary school children have not yet been adequately addressed, because longitudinal data have mostly been limited to two measurement time points. The present study aims to fill this gap by investigating whether cognitive abilities can predict math success across time (unidirectional effect) or whether a reciprocal effect according to the theory of mutualism is more in line with longitudinal data. It also provides information on the stability of intelligence and mathematics achievement in primary school children. Taking into account four measurement occasions, cognitive ability, and achievement in math, N = 1726 primary school students were annually examined. We analyzed construct-specific latent variables and cross-lagged effects over four years. Results indicate a unidirectional cross-lagged relationship pattern rather than a mutual effect between reasoning ability and math achievement. However, over time, the influence of math achievement on cognitive ability increases slightly, which stresses the importance of knowledge acquisition in math for cognitive development over time, and a fairly high stability of cognitive ability and mathematics achievement in primary school age. Full article

We tested 75 participants in a selective university near the East Coast of the United States for their skills in scientific reasoning. We used scientific reasoning assessments for Generating Hypotheses, Generating Experiments, and Drawing Conclusions. To measure scientific reasoning skills, we also used a task involving analyzing scientific impact based on titles of published studies (which were either highly cited or scarcely cited), and another task involving creating what participants believed might be high-impact scientific studies in three subject matter areas. Participants further completed two fluid intelligence tests: Number Series and Letter Sets. They also filled in demographic information, including self-reported SAT/ACT scores and college GPA. (We cannot obtain actual grades at our university because of student-confidentiality issues.) We found that the scientific reasoning tests for Generating Hypotheses, Generating Experiments, and Drawing Conclusions clustered into a single factor, and the task for creating high-impact studies was also factored with these scientific reasoning tests. The two fluid ability tests—Number Series and Letter Sets—clustered into a distinct single factor. The task of analyzing impact seemed to be in between the other tasks, showing characteristics of not only the scientific reasoning tasks but also of the fluid intelligence tasks. Full article

This study presents a comprehensive bibliometric and thematic analysis of social and emotional learning (SEL) research in primary education, aiming to map its evolution, key contributors, and conceptual structure. Drawing on 915 peer-reviewed articles published between 1983 and 2025, retrieved from Web of Science and Scopus, the analysis employed performance metrics, science mapping, and thematic clustering techniques. Findings reveal a marked acceleration in SEL publications since the mid-2010s, with the United States, United Kingdom, and Australia leading both in research output and collaborative networks. Science mapping identified concentrated author and institutional clusters, while also highlighting geographic disparities in global research participation. Thematic analysis uncovered a shift from early focuses on behavioral and emotional regulation toward systemic, school-based interventions emphasizing mental health, resilience, professional development and family engagement. Clustering results positioned ‘social-emotional learning’ as the densest yet fragmented basic theme, reflecting its structural centrality alongside persistent conceptual dispersion across intervention models, implementation processes, and target populations. This study’s findings offer a macro-level synthesis of the SEL research landscape in primary education with the related implications being discussed. Full article