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Exploratory machine-learning classification of student performance in physical education using fitness, engagement, and academic indicators
Princes Lloyda Peralta1, Shekiro Raposas2, Reinalyn Bucsit31. General Education Department, College of Inclusive Education, Lorma Colleges , PH2. College of Computer Studies and Engineering, Lorma Colleges , PH3. General Education Department, College of Inclusive Education, Lorma Colleges , PH2026-09-09|page 276-289AbstractBackground: Physical Education (PE) performance is multidimensional and may be associated with fitness, engagement, attendance, motivation, and prior achievement. Machine learning can identify classification patterns across these indicators, but predictive claims require clear temporal ordering and rigorous validation.
Objective: This study compared four machine-learning classifiers for categorizing student PE performance while minimizing target leakage and addressing class imbalance.
Methods: An exploratory, leakage-controlled classification analysis used a public Kaggle dataset of uncertain provenance. The dataset contained 500 records, 19 substantive variables, and one identifier, with Performance categorized as Low, Average, or High Performer. Because the label-construction rule was undocumented, Performance was treated as an inherited categorical outcome. Overall PE Performance Score, Improvement Rate, and Final Grade were excluded because they could contain current, change-based, or outcome-related information. Categorical predictors were one-hot encoded, numeric predictors were standardized, and preprocessing was performed within training folds. Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression were evaluated using stratified five-fold cross-validation with class-weighted learning.
Results: Random Forest achieved the highest accuracy (0.856), whereas Logistic Regression achieved the highest balanced accuracy (0.749) and macro-F1 (0.645). Its out-of-fold recall was 0.759 for Low, 0.720 for Average, and 0.750 for High Performers, indicating that accuracy alone may inadequately reflect performance in an imbalanced multiclass setting.
Conclusion: Leakage-controlled, class-aware evaluation is essential in machine-learning classification of student performance. These findings are exploratory and should not be interpreted as validated prospective predictions. Future studies should use documented data, explicit temporal ordering, larger balanced samples, and independent external validation.
APA Citation StylePeralta, P. L. , Raposas, S. , & Bucsit, R. (2026). Exploratory machine-learning classification of student performance in physical education using fitness, engagement, and academic indicators. Physical Education and Sports: Studies and Research, 276-289. https://journal.rezkimedia.or.id/pes/article/view/790 -
The effect of resistance band training on the forehand smash speed of youth male badminton singles players
Danan Satria Rahadhi1, Yopi Kusdinar2, Patriana Nurmansyah Awwaludin3, Mulyana Mulyana 41. Indonesia University of Education , ID2. Indonesia University of Education , ID3. Indonesia University of Education , ID4. Indonesia University of Education , ID2025-04-30|page 60-70AbstractBackground: Forehand smash shots kill opponents with explosive blows in a badminton game. In hitting a forehand smash, there are several problems, one of which is the speed of the punch.
Objectives: This study is designed to investigate and identify alternative exercises that can enhance the speed of smash shots. The findings of this study are expected to provide valuable insights for athletes and trainers, aiding in the development of effective training programs.
Methods: The research method used in this study is an experimental method with a quantitative approach and a Two Group Pretest-Posttest Design research design. The population in this study were PB Pasundan Bandung badminton athletes, totaling 30 athletes, with a sample of 20 people using a purposive sampling technique. The instrument in this study used a smash shot speed test using the Velocity Speed Gun tool. The data was processed and analyzed using the normality test with Shapiro-Wilk and then analyzed using the independent t-test with SPSS 23.
Results: The T-test value is with a Sig value. (2-tailed) of 0.00, based on the test results, the Sig value. (2-tailed) <0.05 so that H0 is rejected. So, resistance band training has a significant effect on increasing the speed of forehand smash shots.
Conclusions: In conclusion, the consistent use of resistance bands in training sessions could be a game-changer in boosting the speed of forehand smash shots for badminton athletes. These results strongly advocate integrating resistance band training into early-stage badminton training programs. Future studies could further explore its long-term effects and application across different age groups, fostering hope for the continued evolution of training methods in badminton.
APA Citation StyleRahadhi, D. S. , Kusdinar, Y. , Awwaludin, P. N. , & Mulyana , M. (2025). The effect of resistance band training on the forehand smash speed of youth male badminton singles players. Physical Education and Sports: Studies and Research, 60-70. https://doi.org/10.56003/pessr.v4i1.524 -
Reclaiming the value of qualitative analysis in sports performance research
Spyridon Plakias11. Department of Physical Education and Sport Science, University of Thessaly , GR2026-03-03|page 1-6AbstractQuantitative metrics and technology-driven approaches have reshaped sports performance analysis (SPA), yet they risk overshadowing the equally vital contribution of qualitative methods. This opinion paper highlights the value of qualitative analysis in capturing the complexity, context, and meaning of player and team behaviors, elements that numbers alone cannot explain. Drawing on video-based evaluations, case studies, and narrative reconstructions, qualitative approaches provide insights into tactical, psychological, and interactional dimensions of performance that are indispensable for bridging the science–practice gap. By integrating qualitative and quantitative perspectives, SPA can achieve a more balanced and applicable paradigm, ensuring that scientific research remains relevant to real-world coaching and athlete development. This paper calls for a renewed recognition of qualitative methods as a central pillar of SPA and invites further scholarly contributions to this evolving discussion.
APA Citation StylePlakias, S. (2026). Reclaiming the value of qualitative analysis in sports performance research. Physical Education and Sports: Studies and Research, 1-6. https://doi.org/10.56003/pessr.v5i1.672 -
Integrity in the age of AI: navigating authorship, accountability and ethical publishing
Yulingga Nanda Hanief11. State University of Malang , ID2026-04-03|page 106-112AbstractThe rapid emergence of generative artificial intelligence (AI) is reshaping the landscape of academic writing and scholarly publishing, offering both opportunities and ethical challenges. This editorial reflects on the growing implications of AI-assisted authorship, particularly regarding authorship, accountability, and research integrity. While AI tools can improve clarity and accessibility, their misuse raises concerns about the erosion of critical thinking and the reliability of scientific results.
A key issue highlighted in this discussion is the increasing incidence of false or unverifiable references generated through AI-assisted processes. These practices threaten the credibility of scholarly communication and increase the burden on editors and reviewers. In response, this editorial emphasizes the importance of responsible AI use, transparency in authorship, and rigorous source verification.
Furthermore, the article outlines the journal's commitment to maintaining high standards of quality and ethical publishing. This includes strengthening editorial policies, promoting integrity in manuscript preparation, and encouraging accountability among authors. The editorial also calls on the academic community to adopt responsible practices in the use of emerging technologies.
In line with international publishing standards, this editorial is designed to provide critical perspectives, articulate editorial responsibilities, and offer a forward-looking stance on ethical scientific communication. Ultimately, maintaining trust and credibility in the AI era requires a collective commitment to transparency, rigor, and integrity at every stage of the publication process.
APA Citation StyleHanief, Y. N. (2026). Integrity in the age of AI: navigating authorship, accountability and ethical publishing. Physical Education and Sports: Studies and Research, 106-112. https://doi.org/10.56003/pessr.v5i1.720 -
Determinants of athlete motivation and engagement in collegiate team sports: Toward a sports development framework
Teejay D. Panganiban1, Samboy Nabor21. Batangas State University TNEU JPLPC-Malvar , PH2. University of Perpetual Help System Dalta Las Piñas , PH2026-04-14|page 147-159AbstractBackground: Athlete motivation and sustained engagement are essential for retention in collegiate sports programs; however, the institutional and environmental factors that influence these outcomes remain underexplored.
Objectives: This study aimed to examine the determinants of athlete motivation and engagement in collegiate team sports using a Self-Determination Theory perspective, with engagement serving as a proxy for retention behavior.
Methods: A mixed-methods design was employed involving 75 collegiate student-athletes representing basketball, volleyball (men and women), baseball, and softball teams. Quantitative data were collected using a validated, researcher-developed questionnaire based on Self-Determination Theory and analyzed using descriptive statistics, Pearson's correlation, multiple regression, and Structural Equation Modeling (SEM). In contrast, qualitative data were analyzed using thematic analysis.
Results: Findings revealed that organizational commitment (M = 3.42) and environmental support (M = 3.47) were rated very high, while incentives and facilities were rated high. All program components were significantly associated with athlete engagement (p < .05). Regression analysis showed that environmental support (β = .36) and perceived support (β = .31) were the strongest predictors, explaining 68% of the variance (R² = 0.68). SEM results indicated a good model fit (CFI = 0.95; RMSEA = 0.061).
Conclusions: Qualitative findings highlighted intrinsic motivations, such as passion and belonging, as well as extrinsic motivators, including scholarships and rewards, alongside barriers, including financial delays, academic conflicts, and burnout. The study concludes that environmental and perceived support are the primary determinants of athlete engagement, emphasizing the critical role of competence-supportive environments in sustaining motivation and improving athlete retention.
APA Citation StylePanganiban, T. D. , & Nabor, S. (2026). Determinants of athlete motivation and engagement in collegiate team sports: Toward a sports development framework. Physical Education and Sports: Studies and Research, 147-159. https://doi.org/10.56003/pessr.v5i1.718
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