Journal of Intelligence最新文献

Complex motor tasks that integrate cognitive demands may particularly enhance executive functions, which support school success. Yet few school-based trials have tested structured interventions combining motor complexity and cognitive challenge in early adolescence.

Purpose: This study examined the effects of a gamified "Dual-Challenge Circuit" (DCC), integrating motor patterns with cognitive tasks, on executive functions, academic performance, motor skills, and physical fitness among middle school students. Secondary aims were to explore whether executive functions mediated academic gains and whether a dose-response relationship emerged.

Method: A cluster-randomized controlled trial was conducted in four middle schools in Southern Italy with sixth- and seventh-grade students. Participants were assigned to either the DCC program or traditional physical education. The 12-week intervention included two weekly 60 min sessions. Outcomes were executive functions (Stroop, Digit Span backward, Trail Making Test-B), academic achievement (grades, MT tests), motor coordination (KTK), physical fitness (PACER, long jump, sit-and-reach), and adherence/fidelity.

Results: The DCC group showed significantly greater improvements in all executive function measures and in mathematics and language grades (medium-to-large effects). Mediation analyses confirmed executive functions predicted academic improvements. Motor coordination and fitness also improved, with large effects in aerobic capacity and strength.

Conclusions: The DCC effectively enhanced executive functions, academic outcomes, and fitness. Gamified, cognitively demanding physical education formats appear feasible and beneficial in real-world school settings.

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.

In psychology, small sample sizes are a frequent challenge-particularly when studying specific expert populations or using complex and cost-intensive methods like human scoring of creative answers-as they reduce statistical power, bias results, and limit generalizability. They also hinder the use of frequentist confirmatory factor analysis (CFA), which depends on larger samples for reliable estimation. Problems such as non-convergence, inadmissible parameters, and poor model fit are more likely. In contrast, Bayesian methods offer a robust alternative, being less sensitive to sample size and allowing the integration of prior knowledge through parameter priors. In the present study, we introduce small-sample-size structural equation modeling to creativity research by investigating the relationship between creative fluency and nested creative cleverness with right-wing authoritarianism, starting with a sample size of N = 198. We compare the stability of results in frequentist and Bayesian SEM while gradually reducing the sample by n = 25. We find that common frequentist fit indexes degrade below N = 100, while Bayesian multivariate Rhat values indicate stable convergence down to N = 50. Standard errors for fluency loadings inflate 40-50% faster in frequentist SEM compared to Bayesian estimation, and regression coefficients linking RWA to cleverness remain significant across all reductions. Based on these findings, we discuss (1) the critical role of Bayesian priors in stabilizing small-sample SEM, (2) the robustness of the RWA-cleverness relationship despite sample constraints, and (3) practical guidelines for minimum sample sizes in bifactor modeling.

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.

The present study aimed to investigate individual differences in practice effects during progressive matrices based on Carpenter et al.'s taxonomy of abstract rules. To this end, data from a non-verbal reasoning test, the Abstract Reasoning Test (ART), were used. Because the ART was developed from Carpenter et al.'s theory, the impact of extraneous factors unrelated to the theoretical model is minimized, thereby allowing for a more precise identification of practice effects. The sample consisted of 765 military recruits who responded to 34 items on the ART. Analyses were conducted using a random weights operation-specific learning model (RWOSLM), in which practice parameters were treated as random effects allowed to vary across individuals. The model measures within-test practice effects specific to each examinee, allowing the hypothesis of rule learning during the ART to be assessed at the individual level. Correlations between practice effects and external measures associated with intelligence were examined to investigate the nature of the practice effects. The results suggest individual differences in rule learning within the ART. Decreases in difficulty were observed for both pairwise progression and figure addition or subtraction, although between-person variability was evident only for the latter. Additionally, the results revealed between-person variability in decreases in difficulty associated with one of the items' figural properties, which suggests the existence of individual differences in the process of increasing familiarity with this feature throughout the test. Individual differences in practice effects during the ART significantly correlated with external measures of abilities and intellect, suggesting that practice effects during progressive matrices are conceptually tied to intelligence.

This study examined the associations among emotional intelligence (EI), grit, motivation, and second language (L2) learning performance, with particular attention to the mediating roles of grit and motivation. A sample of 801 Chinese university students completed validated measures of EI, grit, and motivation, and their English test scores were collected as an indicator of performance. Using structural equation modeling (SEM), EI was positively associated with grit (β = 0.574, p < .001) and motivation (β = 0.426, p < .001), while grit was also positively related to motivation (β = 0.515, p < .001). In relation to performance, significant positive associations were observed with motivation (β = 0.635, p < .001), EI (β = 0.185, p < .001), and grit (β = 0.165, p < .001). Bootstrap analyses confirmed robust indirect associations of EI with performance through grit (β = 0.095), through motivation (β = 0.271), and via the sequential chain EI → grit → motivation → performance (β = 0.188). The model accounted for 33% of the variance in grit, 55% in motivation, and 79% in performance. These findings indicate that EI, grit, and motivation are closely interrelated constructs that jointly contribute to L2 performance, highlighting the importance of socio-emotional and motivational resources in second language learning.

Computational thinking (CT) is recognized as a core competency for the 21st century, and its development is shaped by multiple factors, including students' individual characteristics and their use of information and communication technology (ICT). Drawing on large-scale international data from the 2023 cycle of the International Computer and Information Literacy Study (ICILS), this study analyzes a sample of 81,871 Grade 8 students from 23 countries and one regional education system who completed the CT assessment. This study is the first to apply a predictive modeling framework that integrates two machine learning techniques to systematically identify and explain the key variables that predict CT and their nonlinear effects. The results reveal that various student-level predictors-such as educational expectations and the number of books at home-as well as ICT usage across different contexts, demonstrate significant nonlinear patterns in the model, including U-shaped, inverted U-shaped, and monotonic trends. Compared with traditional linear models, the SHapley Additive exPlanations (SHAP)-based approach facilitates the interpretation of the complex nonlinear effects that shape CT development. Methodologically, this study expands the integration of educational data mining and explainable artificial intelligence (XAI). Practically, it provides actionable insights for ICT-integrated instructional design and targeted educational interventions. Future research can incorporate longitudinal data to explore the developmental trajectories and causal mechanisms of students' CT over time.

Dyslexia, a heterogeneous neurodevelopmental disorder, is characterized by persistent reading and spelling difficulties despite average intellectual potential. Although intellectual functioning in dyslexia is often described as average, emerging evidence suggests meaningful within-group variability. This study examined whether children and adolescents with dyslexia exhibited distinct intellectual profiles based on the Stanford-Binet Intelligence Scales, Fifth Edition (SB5). Data were obtained from a large, diagnostically verified sample of 3458 individuals aged 10-19 years assessed in psychological-pedagogical counseling centers across Poland. We used latent profile analysis (LPA) of all 10 SB5 subtests and compared models that specified 2-6 latent classes. The optimal solution identified two profiles: (a) a small subgroup (5%) with globally reduced intellectual functioning and a profound deficit in verbal working memory (>3 standard deviations below the norm) and (b) the predominant subgroup (95%) with broadly average intellectual functioning and relatively preserved reasoning abilities. Profile membership was associated with socioeconomic status; the low-functioning subgroup was associated with lower parental education and age, as younger participants were more likely to belong to this group. These findings highlight the dimensional nature of intellectual heterogeneity in dyslexia and underscore the diagnostic value of profile-based approaches over global intelligence quotient (IQ) scores.

Previous studies suggested that people have stable conflict management styles which relate to their personality traits. However, recent research indicates that conflict management requires flexibility to switch between strategies and that this flexibility may relate to cognitive abilities. The interplay between stable preferences and a flexible performance in conflict management is a novel research avenue. We analyzed data from four studies (N = 1104) using a situational judgment test on emotional intelligence that presents conflict situations in the workplace. We tested whether preferences (selecting one's typical behavior) mainly relate to personality traits and performance (selecting the ideal behavior) to cognitive abilities. We found that preferences akin to conflict management styles emerged between individuals; however, these preferences did not reflect tangible differences in personality traits. Considering performance, cognitive abilities were consistently conducive to solving conflicts, while the contribution of personality traits varied across situations, with the possibility that some traits may even hinder conflict resolution in some situations. We provide preliminary evidence on the contributions of both traits and abilities to conflict management, arguing that conflict management research needs to adopt a holistic view that combines both a person's traits and abilities to explain their conflict behavior.

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.