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    Effects of cooperative learning on students' learning outcomes in physical education: a meta-analysis
    (Frontiers Media Sa, 2025) Boke, Hulusi; Aygun, Yalin; Tufekci, Sakir; Yagin, Fatma Hilal; Canpolat, Burak; Norman, Goktug; Ardigo, Luca Paolo
    This meta-analysis examines the effect of Cooperative Learning (CL) interventions, compared to traditional instructional methods, on students' learning outcomes across affective, cognitive, physical, and social domains in physical education (PE). The review involved a comprehensive search of 12 databases in English, Spanish, and Turkish, with the last search conducted on June 2nd, 2024. Studies included were true experimental or quasi-experimental designs featuring direct CL interventions in PE, covering students of both genders from primary school to university levels. The standardized Cochrane methods were used to identify eligible records, collect and combine data, and assess the risk of bias. Comprehensive Meta-Analysis (CMA) v4 software package was used to yield a summary of quantitative results. Hedges's g was used as the effect size (ES) measure, calculated from pre- and post-tests in both experimental and control groups. Forty-three studies (comprising 60 reports) were initially included, but three studies were excluded as outliers, leaving 40 studies (56 reports) with a total of 3.985 participants for analysis. The random effects model revealed a moderate positive overall effect of CL interventions (ES = 0.459, 95% CI = [0.324, 0.592], p < 0.001), indicating that CL enhances PE students' learning across four domains. Subgroup analyses showed small to moderate ESs for affective (ES = 0.304), physical (ES = 0.471), cognitive (ES = 0.589), and social learning (ES = 0.612). Risk of bias was evaluated using Begg and Mazumdar's rank correlation, the classic fail-safe number, and a funnel plot, all indicating a low risk of bias. Methodological quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). The study was registered on PROSPERO (ID: CRD42024532607). This meta-analysis underscores the effectiveness of CL as a student-centered pedagogical model in PE, demonstrating its positive effect on various learning outcomes in the affective, cognitive, physical, and social domains. The findings provide instructive data and strategies for researchers, practitioners, and policymakers aiming to integrate, implement, or make context-specific adaptations of CL into educational processes, while ESs in the affective, physical, cognitive, and social learning domains provide domain-based implementation guidance for these stakeholders.
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    Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence
    (Frontiers Media Sa, 2025) Gormez, Yasin; Yagin, Fatma Hilal; Yagin, Burak; Aygun, Yalin; Boke, Hulusi; Badicu, Georgian; Aghaei, Mohammadreza
    Objectives This study aims to build a machine learning (ML) prediction model integrated with explainable artificial intelligence (XAI) to categorize obesity levels from physical activity and dietary patterns. The inclusion of XAI methodologies facilitates a comprehensive understanding of the risk factors influencing the model predictions and thus increases transparency in the identification of obesity risk factors.Methods Six ML models were used: Bernoulli Naive Bayes, CatBoost, Decision Tree, Extra Trees Classifier, Histogram-based Gradient Boosting and Support Vector Machine. For each model, hyperparameters were tuned by random search methodology and model effectiveness was evaluated by repeated holdout testing. SHAP (SHapley Additive Annotations) and LIME (Local Interpretable Model Independent Annotations) interpretability methods were used to generate local and global feature importance measures.Results The CatBoost model exhibited the highest overall performance and achieved superior results in accuracy, precision, F1 score and AUC metrics. Nonetheless, other models such as Decision Tree and Histogram-based Gradient Boosting also yielded strong and competitive results. The results also highlighted age, weight, height and specific food patterns as key predictors of obesity. In terms of interpretability, LIME showed superior in fidelity, whereas SHAP showed improved sparsity and consistency across models, facilitating a comprehensive understanding of trait importance.Conclusion This research demonstrates that ML algorithms, when integrated with XAI technologies, can accurately predict obesity levels and explain important contributing risk factors. The use of SHAP and LIME increases model transparency, facilitating the identification of specific lifestyle patterns linked to obesity risk. These findings help to formulate more precise intervention techniques guided by a reliable and understandable predictive framework.