This systematic review analyses the evolution of gifted education in England between 2010 and 2025. The year 2010 serves as a critical turning point, characterized by the withdrawal of the national Gifted and Talented (G&T) policy and the subsequent delegation of identification and provision responsibilities to schools. This change created a gap in the literature due to a lack of focused research examining the challenges and deficiencies that emerged following this policy shift. This study is original in that it is the first to bridge existing implementation gaps and provide a robust evidence base for future educational policies. The review focuses on policy frameworks, identification models, and socio-emotional outcomes. Following the PRISMA guidelines, fifteen peer-reviewed studies retrieved from Web of Science, Scopus, and Google Scholar were examined through thematic synthesis. Findings indicate a persistent gap between policy rhetoric and classroom practice. Identification processes remain heavily reliant on standardized testing and teacher judgment, often neglecting creativity, diversity, and contextual factors. Fragmented teacher training limits the ability to effectively support gifted learners, particularly those from disadvantaged or twice exceptional (2e) backgrounds. Socio-emotional outcomes reveal that academic success does not guarantee emotional well-being, highlighting the prevalence of perfectionism and stigmatization. These findings underscore the need for teachers and teacher educators to strengthen pre- and in-service training, so they can better recognize diverse forms of giftedness and support students' socio-emotional needs through more equitable and research-informed practices.

Stress is a major risk factor for creativity development in adolescents. This study explored the protective effect of openness on creative tendency under stress and revealed the underlying mechanisms from the perspectives of stress perception and cognitive flexibility. A total of 1489 junior high school students (Mage = 13.65 years, SD = 0.74) participated in the study. The results showed that stress perception and cognitive flexibility sequentially mediated the negative effect of stressors on creative tendency, and openness moderated this process. Individuals with high openness had lower stress perception and higher cognitive flexibility at the same level of stressors, thus showing a higher creative tendency. However, the protective effect of openness diminished as the stress level increased. We concluded that openness could buffer the negative effects of stress on creative tendency to some extent. These findings highlight the importance of positive personality traits and provide a theoretical guide for cultivating creative qualities.

Self-regulated learning (SRL) is a critical competency for learners in increasingly technology-enhanced educational environments, yet little is known about how SRL is fostered within video-based interventions in K-12 settings. While existing reviews and meta-analyses focus on the effectiveness of SRL interventions, this study aims to address current gaps by specifically examining the implementation processes, instructional tools, and the role of video. Addressing this, the present study conducted a systematic literature review of peer-reviewed K-12 intervention studies published since 2010, guided by PRISMA standards and other methodological frameworks in the field of SRL. 30 quantitative or mixed-methods studies focusing on K-12 SRL interventions were selected from Web of Science and ERIC, with the requirement that video served as an instructional component rather than a research tool. These studies were then systematically coded by eight researchers for SRL strategies, instructional methods, video roles, and pedagogical settings. Findings show that most video interventions targeted multiple SRL strategies across different phases of the SRL cycle, offering a comprehensive approach to fostering regulation. However, while cognitive and metacognitive strategies were frequently addressed, motivational and resource-management strategies were seldom included within explicit instruction, which focused primarily on cognitive and metacognitive training. Video played multiple pedagogical roles, including delivering disciplinary content, teaching SRL strategies, or combining both. A thematic analysis identified four pedagogical settings that characterized successful interventions: Teacher-guided, Active, Social, and Knowledge-based (TASK) learning. These settings appear to mitigate common challenges of video-based learning, such as cognitive load and learner passivity. The review contributes a novel synthesis of SRL-video integration and proposes TASK learning as a framework for designing SRL interventions.

The likelihood-based person-fit statistic, lz*, is commonly used in educational assessments to distinguish between respondents who are putting in effort and those who are not. However, lz* depends on the estimated item parameters. Item parameter estimates based on data containing non-effortful respondents are biased, thereby undermining the strength of lz*. To address this issue, we propose a two-step method that leverages data mining techniques to obtain more accurate item parameter estimates and then uses them to compute lz*. The results show that the estimates based on the effortful group identified by K-means are more accurate, which improves the performance of lz* in terms of the precision of identifying effortful respondents when non-effort severity is high.

The author investigates how digital learning competence (DLC) is bridged to academic achievement (AA) through informal digital learning engagement (IDLE) and how meta-cognitive self-regulation (MSR) shapes these pathways among university students. Grounded in a moderated mediation framework, this research conceptualizes DLC not as a static skill set but as a latent capacity that is channeled into academic outcomes when students autonomously engage in digital environments and regulate their cognition. Survey data were collected from 432 undergraduate students and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that DLC significantly predicts AA both directly and indirectly via IDLE, identifying informal digital learning engagement as a central pathway through which digital learning competence is translated into academic gains. Furthermore, MSR moderates the relationship between DLC and IDLE, such that higher levels of metacognitive self-regulation strengthen the conversion of digital learning competence into productive informal digital learning engagement. These findings support a dynamic view of digital learning competence and underscore the roles of learner autonomy and metacognitive awareness in transforming digital skills into meaningful educational outcomes. By integrating perspectives on digital literacy, self-regulated learning, and informal learning, this study offers implications for the design of digital learning ecosystems that effectively bridge students' digital capacities with their academic achievement.

Music is more than just entertainment. It is a complex auditory stimulus that engages various cognitive processing systems. Accordingly, natural music-listening patterns may reveal insights into individual differences in general cognitive ability (GCA). In this study (N = 185), we used real-world smartphone-based music-listening records collected over five months to explore this question. We quantified participants' listening habits (e.g., listening durations) and music preferences based on audio characteristics (e.g., tempo, mode) and lyrical characteristics (e.g., positive emotion words, affiliation words) of the songs they had listened to. These strictly behavioral features were used to predict GCA scores using linear LASSO regression and nonlinear random forest models. Out-of-sample cross-validation indicated modest predictive performance, with only the random forest model detecting small but reliable associations between music-listening behavior and GCA. Interpretable machine learning analyses showed that lyrics-based preferences were the most informative feature group, followed by listening habits, whereas audio characteristics contributed little predictive value. We discuss how these findings offer initial evidence that cognitive ability may be reflected, albeit subtly, in micro-patterns of everyday, non-achievement-related behavior, and outline conceptual and methodological challenges for future work using digital behavioral data to complement traditional cognitive assessment.

Graphs are used in school, many occupations, and daily life, yet many people struggle to interpret them accurately. To help identify sources of difficulty in graph comprehension, we propose the Pictorial-Semantic-Task Framework. In it, we argue that accurate interpretation of graphs requires integrating pictorial variables (e.g., slope direction, graph format, data points) with semantic variables (e.g., titles, labels, scales, variable types) to determine what the graph represents. Many errors arise because readers fail to coordinate these two sources of information, often basing interpretations solely on pictorial variables. The present theoretical synthesis presents the basic analysis underlying the Pictorial-Semantic-Task Framework and an integrative review of relevant findings from graph encoding, extrapolation, and comparison tasks. The findings show that people encode and recall pictorial information far more accurately than semantic information, and often base interpretations solely on visual patterns even when semantic features call for a different conclusion. Analyses of U.S. textbooks and mass media reveal potential sources of these biased interpretations: systematic imbalances in the types of semantic information provided in textbooks and media seem likely to contribute to biases, emphasizing visual over semantic cues. By describing how perceptual and conceptual processes interact during graph comprehension, we aim to advance theories of cognitive processing in the context of graph comprehension and to derive educational implications for helping children interpret graphs more accurately.

Polymathy relates to the exceptional learning abilities, in which individuals cultivate and coordinate Breadth, Depth, and integrative capability across multiple domains. It builds on mechanisms typically associated with intelligence, including abstraction, problem solving, and the transfer and integration of information. Because polymathic disposition has partial biological underpinnings, it may intersect with other biologically based individual differences, such as temperament. Biographical accounts also indicate that many polymaths did not achieve exceptional school grades, raising questions about whether the multiplicity of interests in polymaths is associated with distractibility and impulsivity, or whether there is a deeper institutional mismatch between polymaths and educational systems. Our study examined these issues using estimated high school grades across three subject areas, documented university grades, a neurochemistry-validated temperament assessment (Structure of Temperament Questionnaire; STQ-77), the Trait Polymathy Scale (TPS), the Barratt Impulsivity Scales (BIS-11), and information about aptitudes and interests from 296 participants (M/F = 152/144). Contrary to speculation that polymathy reflects distractibility, the TPS correlated negatively with the BIS-11 Lack of Attention scale and positively with the STQ-77 scales of Intellectual Endurance and Probabilistic Processing, confirming high sustained attention in polymaths. TPSs also had selective negative correlations with the STQ-77 Neuroticism scale and positive correlations with the STQ-77 Plasticity, Social Endurance, Sensation Seeking, dispositional Satisfaction scales, as well as several specific and general aptitudes and interests. These findings refine the dispositional profile linked to polymathy, highlighting the differential nature of the three components of polymathy.

This study investigates the distinct mechanisms of human versus Large Language Model (LLM) creativity. Employing a two-stage experimental design, we systematically compared Human-Only, LLM-Only, and LLM-Assisted performance across propositional and creative writing tasks. Results revealed a critical asymmetry contingent upon the research context: human authors exhibited higher originality in high-demand creative tasks, whereas LLMs governed execution quality, maintaining superior effectiveness across different tasks and cohorts. This pattern is characterized by four exploratory writing creativity profiles: Ideal, Safe, Moderate, and Plain. The distribution of human and LLM writings across these profiles was strikingly different. Hierarchical Moderated Regression analysis uncovered divergent linguistic pathways: human originality is predicted by markers of subjective cognitive investment, while LLM effectiveness is mechanistically driven by optimized structural coherence. Furthermore, the study identified a "Collaboration Trap" during collaboration with a suboptimal LLM. This partnership failed to improve human performance relative to LLM-Only benchmarks and induced cognitive anchoring, leading humans to mimic AI complexity without quality gains. These insights offer critical implications for preserving human agency and avoiding homogenization in future human-AI collaborative writing pedagogies.

In this study, we aimed to investigate the cognitive and affective-emotional factors underlying math achievement in a sample of 169 Japanese elementary school children. Using structural equation modeling, we examined the contributions of fluid and crystallized intelligence, verbal and spatial working memory, and affective-emotional variables, including general anxiety, test anxiety, math anxiety, and math self-efficacy. We found intelligence to be a strong positive predictor of math achievement, while among the affective-emotional variables, math self-efficacy emerged as the only significant predictor of math achievement. Interestingly, intelligence mediated the association between affective-emotional factors, such as math anxiety and self-efficacy, highlighting its central role in children's math achievement. These findings underscore the strong relationship between intelligence and self-efficacy in educational contexts, suggesting that self-efficacy is closely linked to cognitive abilities to support children's success in math. Educational implications are discussed, emphasizing the need to strengthen math self-efficacy alongside cognitive abilities.