Evaluating Online Learning Management Systems Effectiveness for Student Learning Outcomes in K-12 Education

Authors

  • LuRuiJing Universitas Pendidikan Ganesha, China
  • I Wayan Sukra Warpala Universitas Pendidikan Ganesha, Indonesia
  • Ni Nyoman Parwati Universitas Pendidikan Ganesha, Indonesia

DOI:

https://doi.org/10.33394/jp.v13i4.21822

Keywords:

Academic Achievement, Educational Gap, Learning Engagement, Learning Management System (LMS), Teacher Professional Support

Abstract

This study aims to examine the effectiveness of Learning Management Systems (LMSs) in relation to student learning outcomes by investigating the relationships among LMS usage, academic achievement, learning engagement, educational disparities, and teacher professional support in K–12 education. A quantitative cross-sectional correlational design was employed, involving 240 participants, comprising 200 students and 40 teachers from public and private schools in Java, Indonesia, who were selected through stratified random sampling. Data were collected using validated five-point Likert-scale questionnaires measuring LMS usage, learning engagement, educational disparities, and teacher professional support, while students’ academic achievement data were obtained from official school assessment records. The data were analyzed using descriptive statistics, Pearson correlation analysis, and multiple linear regression. The findings revealed that LMS usage was positively associated with academic achievement (r = .42, p < .01), learning engagement (r = .47, p < .01), and teacher professional support (r = .39, p < .01), and negatively associated with educational disparities (r = −.38, p < .01). Multiple regression analysis identified LMS usage as the strongest predictor across the regression models, with the largest standardized coefficients for academic achievement (β = .28, p < .001), learning engagement (β = .31, p < .001), and educational disparities (β = −.24, p < .001). Teacher professional support also made significant contributions to academic achievement and learning engagement and was associated with reduced educational disparities. The findings suggest that effective LMS implementation, supported by sustained teacher professional development and equitable access to digital learning resources, may enhance student learning outcomes and contribute to sustainable digital transformation in K–12 education.

References

Ahmed, E. (2024). Student Performance Prediction Using Machine Learning Algorithms. Applied Computational Intelligence and Soft Computing, 1(1), 1–15. https://doi.org/10.1155/2024/4067721

Algerafi, M. A. M., Zhou, Y., & Oubibi, M. (2023). Unlocking the Potential : A Comprehensive Evaluation of Augmented Reality and Virtual Reality in Education. MDPI, 7(2), 1–29. https://doi.org/https://doi.org/10.31681/jetol.1419279

Alotaibi, N. S. (2024). The Impact of AI and LMS Integration on the Future of Higher Education : Opportunities , Challenges , and Strategies for Transformation. MDP, 16(23), 1–21. https://doi.org/10.3390/su162310357

Alshammary, F. M., & Alhalafawy, W. S. (2023). Digital Platforms and the Improvement of Learning Outcomes : Evidence Extracted from Meta-Analysis. MDPI, 15(2), 1–21. https://doi.org/10.3390/su15021305

Damaševiˇ, R. (2024). Virtual Worlds for Learning in Metaverse : A Narrative Review. MDPI, 16(5), 1–41. https://doi.org/https://doi.org/10.3390/su16052032

Dwi Novianti, Cica Yulia, Elih Sudiapermana, R. W. A. R. (2025). Jurnal Paedagogy : Jurnal Paedagogy : Jurnal Paedagogy, 12(3), 969–979. https://doi.org/10.33394/jp.v12i3.16397

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160. https://doi.org/10.3758/BRM.41.4.1149

Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications.

Gkintoni, E., Antonopoulou, H., & Sortwell, A. (2025). Challenging Cognitive Load Theory : The Role of Educational Neuroscience and Artificial Intelligence in Redefining Learning Efficacy. MDPI, 15(2), 1–101. https://doi.org/10.3390/brainsci15020203

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Hassan, A., & Id, R. (2023). The impact of Blackboard Collaborate breakout groups on the cognitive achievement of physical education teaching styles during the COVID-19 pandemic. PLoS ONE, 1–20. https://doi.org/10.1371/journal.pone.0279921

Hongsuchon, T., Emary, I. M. M. El, Hariguna, T., Mohammed, E., & Qhal, A. (2022). Assessing the Impact of Online-Learning Effectiveness and Benefits in Knowledge Management , the Antecedent of Online-Learning Strategies and Motivations : An Empirical Study. MDPI, 14(5), 1–16. https://doi.org/10.3390/su14052570

Id, J. L., & Liang, W. (2024). Effectiveness of virtual laboratory in engineering education : A meta-analysis. PLOS One, 19(12), 1–23. https://doi.org/10.1371/journal.pone.0316269

Jacob, T., & Centofanti, S. (2024). Effectiveness of H5P in improving student learning outcomes in an online tertiary education setting. Journal of Computing in Higher Education, 36(2), 469–485. https://doi.org/10.1007/s12528-023-09361-6

Létourneau, A., Martineau, M. D., Charland, P., Karran, J. A., Boasen, J., & Léger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems ( ITS ) in K-12 education. Npj Science of Learning, 10(1), 1–13. https://doi.org/10.1038/s41539-025-00320-7

Li, H., & Ni, A. (2024). behavioral sciences What Contributes to Student Language Learning Satisfaction and Achievement with Learning Management Systems ? MDPI, 14(4), 1–14. https://doi.org/10.3390/bs14040271

Mahafdah, R., Bouallegue, S., & Bouallegue, R. (2024). Enhancing e-learning through AI : advanced techniques for optimizing student performance. Research Square, 1–25. https://doi.org/10.7717/peerj-cs.2576

Nabiem, C., Stephen, A., Sunday, A., Hilary, A., & Sidney, O. (2024). Impact of online learning on student ’ s performance and engagement : a systematic review. Discover Education, 3(1), 1–15. https://doi.org/10.1007/s44217-024-00253-0

Sun, L., & Song, X. (2026). The effectiveness of virtual reality for K-12 foreign language learning : a systematic review of recent randomized controlled trials. Frontiers in Psychology, 2026(January). https://doi.org/10.3389/fpsyg.2025.1714481

Syifa Khairina Akhlida, Siswanto, A. M. (2026). Jurnal Paedagogy : Jurnal Paedagogy : Jurnal Paedagogy, 13(1), 211–221. https://doi.org/10.33394/jp.v13i1.18331

Topping, K. J., Douglas, W., Robertson, D., & Ferguson, N. (2022). Effectiveness of online and blended learning from schools : A systematic review. July 2021, 1–41. https://doi.org/10.1002/rev3.3353

Xu, Z., Zhao, Y., Zhang, B., Liew, J., & Kogut, A. (2023). A meta-analysis of the efficacy of self-regulated learning interventions on academic achievement in online and blended environments in K-12 and higher education. Behaviour & Information Technology, 42(16), 2911–2931. https://doi.org/10.1080/0144929X.2022.2151935

Yang, J. (2025). Fuzzy comprehensive evaluation system and decision support system for learning management of higher education online courses. Scientific Reports, 15(1), 1–18. https://doi.org/10.1038/s41598-025-01782-w

Published

2026-10-08

How to Cite

LuRuiJing, I Wayan Sukra Warpala, & Ni Nyoman Parwati. (2026). Evaluating Online Learning Management Systems Effectiveness for Student Learning Outcomes in K-12 Education. Jurnal Paedagogy, 13(4). https://doi.org/10.33394/jp.v13i4.21822

Issue

Vol. 13 No. 4 (2026): October (IN PRESS)

Section

Articles

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