Home⇛Journal of Interdisciplinary Perspectives⇛vol. 4 no. 9 (2026)

Instructional Quality, Student Engagement, and Skill Development as Predictors of Learning Outcomes in Online Physical Education: A Structural Equation Model of Senior High School Learners

Mannycho S. Sanayan | Gerald C. Ello

Discipline: sport and exercise sciences

 

Abstract:

The rapid shift to online learning during the COVID-19 pandemic raised critical questions about the effectiveness of digital instruction in subjects that are inherently physical and experiential. This study modeled the relationships among instructional quality, student engagement, physical activity and skill development, and learning outcomes in online physical education (PE) among senior high school students in the Philippines using Structural Equation Modeling (SEM). Four latent constructs were examined: Instructional Quality (IQ), Student Engagement (SE), Physical Activity and Skill Development (PASD), and Learning Outcomes in Physical Education (LOPE). A cross-sectional survey was administered to 171 students drawn from two private schools in southern Metro Manila and Cavite Province. Most participants learned through a synchronous platform, with asynchronous delivery serving a supplementary role; the findings therefore characterize online PE as actually experienced by this cohort rather than a controlled comparison of the two modalities. Data were analyzed through reliability analysis, bivariate correlations, and covariance-based SEM with maximum likelihood estimation using semopy in Python. All four constructs demonstrated strong internal consistency and convergent validity, and discriminant validity was satisfied. The model showed acceptable fit (χ2/df = 2.142, CFI = 0.923, TLI = 0.911, RMSEA = 0.082) and explained 95.9% of the variance in learning outcomes. All six hypothesized paths were statistically significant, with instructional quality emerging as the strongest antecedent of student engagement and student engagement as the strongest antecedent of skill development. Notably, although the structural relationships were uniformly strong, students rated their overall learning outcomes in online PE as low in absolute terms, indicating that a well-specified instructional model does not by itself overcome the constraints of delivering embodied learning through a screen. Findings carry implications for curriculum design and digital pedagogy in blended and online PE.



References:

  1. Anderson, J.C., & Gerbing, D.W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. https://doi.org/10.1037/0033-2909.103.3.411
  2. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
  3. Bentler, P.M., & Chou, C.-P. (1987). Practical issues in structural modeling. Sociological Methods & Research, 16(1), 78-117. https://doi.org/10.1177/0049124187016001004
  4. Creswell, J.W., & Creswell, J.D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
  5. Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  6. Etikan, I., Musa, S.A., & Alkassim, R.S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1-4. https://doi.org/10.11648/j.ajtas.20160501.11
  7. Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  8. Goad, T. (2018). Predicting student success in online physical education (Doctoral dissertation). West Virginia University. https://researchrepository.wvu.edu/etd/5684
  9. Hair, J.F., Jr., Black, W.C., Babin, B.J., & Anderson, R.E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
  10. Harman, H.H. (1976). Modern factor analysis (3rd ed.). University of Chicago Press.
  11. Hu, L., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
  12. Huang, H.-C., Kung, Y.-T., Huang, R.-R., Mui, W.-C., & Su, Y.-C. (2025). Assessment of physical education teachers' use of distance teaching behavior under the influence of the COVID-19 pandemic. PeerJ, 13, e18743. https://doi.org/10.7717/peerj.18743
  13. Kemp, S. (2024). Digital 2024: The Philippines [Report]. DataReportal. https://datareportal.com/reports/digital-2024-philippines
  14. Kline, R.B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
  15. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1-10. https://doi.org/10.4018/ijec.2015100101
  16. Lobo, J., Prevandos, F.G., Tanucan, J.C., & Setiawan, E. (2024). Is video-conferencing helpful for physical education classes in the new normal? A PLS-SEM analysis adopting the technology acceptance model. Journal of Learning for Development, 11(1), 99-114.
  17. MacCallum, R.C., Browne, M.W., & Sugawara, H.M. (1996). Power analysis and determination of sample size for covariance structure modeling. Psychological Methods, 1(2), 130-149. https://doi.org/10.1037/1082-989X.1.2.130
  18. Marsh, H. W., Balla, J. R., & Hau, K.-T. (1996). An evaluation of incremental fit indexes: A clarification of mathematical and empirical properties. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques (pp. 315-353). Lawrence Erlbaum.
  19. Meshcheryakov, G., & Igolkina, A.A. (2021). semopy: A Python package for structural equation modeling. Applied Mathematics, 12(9), 818-831. https://doi.org/10.4236/am.2021.129054
  20. Padua, M., Quesada, A., & Galgo, A., Jr. (2024). Engagement and interest through physical activities among senior high school students at Muntinlupa National High School. International Journal of Research and Innovation in Social Science, 8(Special Issue), 1352-1358. https://doi.org/10.47772/IJRISS.2024.803098S
  21. Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., & Podsakoff, N.P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879
  22. Rihatno, T., & Nuraini, S. (2023). Evaluation of physical and health education online learning in elementary schools: PLS-SEM approach. International Journal of Information and Education Technology, 13(7), 1156-1165. https://doi.org/10.18178/ijiet.2023.13.7.1917
  23. Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. https://doi.org/10.2307/30036540
  24. Wang, C., Yuan, Y., & Ji, X. (2024). The effects of a blended learning model on the physical fitness of Chinese university students: A cluster randomized controlled trial in basketball education. BMC Public Health, 24, 2451. https://doi.org/10.1186/s12889-024-20001-1
  25. Yu, J., & Jee, Y. (2021). Analysis of online classes in physical education during the COVID-19 pandemic. Education Sciences, 11(1), 3. https://doi.org/10.3390/educsci11010003

Tools


Copyright © 2026  KITE Digital Educational Solutions |  Exclusively distributed by CE-Logic