Skip to main navigation menu Skip to main content Skip to site footer


Vol 8 No 2 (2023): December

Self-Regulated Learning Students With Personalized E-Learning
Siswa Pembelajaran Mandiri Dengan E-Learning yang Dipersonalisasi

(*) Corresponding Author
August 15, 2023


The implementation of personalized e-learning at the Muhammadiyah University of Sidoarjo, especially the Faculty of Islamic Religion, has contributed to this trend of electronic-based learning. The use of e-learning has challenges, namely the need to master digital competence and the responsibilities of lecturers in guiding and directing students during online learning. The analysis used mixed ANOVA to determine the increase in student personalization through e-learning design. the results of the study showed that there were differences in the learning outcomes of the control and experimental groups. The results of the one way ANOVA analysis show differences in personalized learning achievement according to students' learning needs and potential. The more students interact and ask questions outside of class, the more actively they are involved in independent learning from home. This condition is not found in conventional e-learning, which is only carried out modestly by lecturers and students. They create a fun and more communicative learning atmosphere complemented by the reliability of adequate e-learning platform.


  • Implementation of personalized e-learning at Muhammadiyah University of Sidoarjo enhances electronic-based learning trend.
  • Challenges include mastering digital competence and lecturer responsibilities in guiding online learning.
  • Mixed ANOVA analysis reveals differing learning outcomes between control and experimental groups.

Keywords: Personalized e-learning, Digital competence, Mixed Anova analysis, Independent learning


  1. N. Khan et al., "Connecting digital literacy in higher education to the 21st-century workforce," Knowledge Management & E-Learning, vol. 14, no. 1, pp. 46–61, 2022.
  2. R. Malhotra et al., "An android application for campus information system," Procedia Computer Science, vol. 172, pp. 863–868, 2020. doi: 10.1016/j.procs.2020.05.124.
  3. J. Nasir et al., "Fake news detection: A hybrid CNN-RNN based deep learning approach," International Journal of Information Management and Data Insights, vol. 1, no. 1, p. 100007, 2021. doi: 10.1016/j.jjimei.2020.100007.
  4. S. Dijkstra et al., "Clustering children's learning behaviour to identify self-regulated learning support needs," Computers in Human Behavior, vol. 145, no. February, p. 107754, 2023. doi: 10.1016/j.chb.2023.107754.
  5. M. Wahid et al., "Implementing project-based learning for sustainability management course at postgraduate level," Asian Journal of University Education, vol. 16, no. 2, pp. 84–92, 2020. doi: 10.24191/AJUE.V16I2.10300.
  6. J. Silva Moreira et al., "Dynamic assessment of self-regulated learning in preschool," Heliyon, vol. 8, no. 8, 2022. doi: 10.1016/j.heliyon.2022.e10035.
  7. H. Al-Chalabi and A. Hussein, "Ontologies and Personalization Parameters in Adaptive E-learning Systems: Review," Journal of Applied Computer Science and Mathematics, vol. 14, no. 1, pp. 14–19, 2020. doi: 10.4316/jacsm.202001002.
  8. T. Lalitha and P. Sreeja, "Personalised Self-Directed Learning Recommendation System," Procedia Computer Science, vol. 171, pp. 583–592, 2020. doi: 10.1016/j.procs.2020.04.063.
  9. M. Coban et al., "Using virtual reality technologies in STEM education: ICT pre-service teachers' perceptions," Knowledge Management & E-Learning, vol. 14, no. 3, pp. 269–285, 2022.
  10. Y. Karlen et al., "Teachers as learners and agents of self-regulated learning: The importance of different teachers competence aspects for promoting metacognition," Teaching and Teacher Education, vol. 125, p. 104055, 2023. doi: 10.1016/j.tate.2023.104055.
  11. E. Aeiad and F. Meziane, "An adaptable and personalised E-learning system applied to computer science Programmes design," Educational Information Technology, vol. 24, no. 2, pp. 1485–1509, 2019. doi: 10.1007/s10639-018-9836-x.
  12. F. Bayrak, "Associations between university students' online learning preferences, readiness, and satisfaction," Knowledge Management & E-Learning, vol. 14, no. 2, pp. 186–201, 2022. doi: 10.34105/j.kmel.2022.14.011.
  13. V. Entwistle et al., "Unifying and universalizing Personalised Care? An analysis of a national curriculum with implications for policy and education relating to person-centred care," Patient Education and Counseling, vol. 105, no. 12, pp. 3422–3428, 2022. doi: 10.1016/j.pec.2022.07.003.
  14. Z. Seitakhmetova et al., "The Study of the Transition To Personalized Learning of Schoolchildren in the Republic of Kazakhstan Based on a Logical-Structural Approach," Journal of Theoretical and Applied Information Technology, vol. 100, no. 7, pp. 1907–1918, 2022.
  15. A. Funa et al., "Exploring Filipino preservice teachers' online self-regulated learning skills and strategies amid the COVID-19 pandemic," Social Sciences and Humanities Open, vol. 7, no. 1, p. 100470, 2023. doi: 10.1016/j.ssaho.2023.100470.
  16. G. dos Santos Leandro et al., "Process mining leveraging the analysis of patient journey and outcomes: Stroke assistance during the Covid-19 pandemic," Knowledge Management & E-Learning, vol. 13, no. 4, pp. 421–437, 2021. doi: 10.34105/j.kmel.2021.13.023.
  17. M. Murtaza et al., "AI-Based Personalized E-Learning Systems: Issues, Challenges, and Solutions," IEEE Access, vol. 10, pp. 81323–81342, 2022. doi: 10.1109/ACCESS.2022.3193938.
  18. S. Ghallabi et al., "Reuse of e-learning personalization components," Smart Learning Environments, vol. 9, no. 1, pp. 7–17, 2022. doi: 10.1186/s40561-022-00214-w.
  19. H. Hashim et al., "The Role of Perceived Usefulness and Confirmation in Influencing Student's Satisfaction on Online Distance Learning," Asian Journal of University Education, vol. 19, no. 2, pp. 294–306, 2023.
  20. A. Bimba et al., "Adaptive feedback in computer-based learning environments: a review," Adaptive Behavior, vol. 25, no. 5, pp. 217–234, 2017. doi: 10.1177/1059712317727590.
  21. T. Ingkavara et al., "The use of a personalized learning approach to implementing self-regulated online learning," Computers and Education: Artificial Intelligence, vol. 3, pp. 1–18, 2022. doi: 10.1016/j.caeai.2022.100086.
  22. W. Zhang et al., "Semi-asynchronous personalized federated learning for short-term photovoltaic power forecasting," Digital Communications and Networks, vol. 2022. doi: 10.1016/j.dcan.2022.03.022.
  23. J. W. Cresswell, Research Design: Qualitative Approaches, Quantitative and Mixed Methods, 4th ed. Sage, 2014.
  24. J. Wang et al., "Factors influencing attitudes toward cyber-counseling among China's Generation Z: A structural equation model," Archives of Psychiatric Nursing, vol. 40, no. 6, pp. 124–131, 2022. doi: 10.1016/j.apnu.2022.07.011.
  25. U. Maier and C. Klotz, "Personalized feedback in digital learning environments: Classification framework and literature review," Computers and Education: Artificial Intelligence, vol. 3, pp. 1–18, 2022. doi: 10.1016/j.caeai.2022.100080.
  26. S. N. Kane et al., "Preface: International Conference on Recent Trends in Physics (ICRTP 2016)," Journal of Physics: Conference Series, vol. 755, no. 1, 2016. doi: 10.1088/1742-6596/755/1/011001.
  27. C. Rinner et al., "Cutaneous Melanoma Surveillance by means of Process Mining," Studies in Health Technology and Informatics, vol. 205, pp. 1208–1212, 2014. doi: 10.3233/978-1-61499-432-9-1208.
  28. R. Wang and Z. Shi, "Personalized Online Education Learning Strategies Based on Transfer Learning Emotion Classification Model," Security and Communication Networks, vol. 2021, p. 11, 2021. doi: 10.1155/2021/5441631.
  29. A. Shemshack and J. M. Spector, "A systematic literature review of personalized learning terms," Smart Learning Environments, vol. 7, no. 1, pp. 2–20, 2020. doi: 10.1186/s40561-020-00140-9.
  30. M. T. C. Lim et al., "School closure during the coronavirus disease 2019 (COVID-19) pandemic – Impact on children's sleep," Sleep Medicine, vol. 78, pp. 108–114, 2021. doi: 10.1016/j.sleep.2020.12.025.
  31. M. Tavakoli et al., "An AI-based open recommender system for personalized labor market driven education," Advances in Engineering Informatics, vol. 52, pp. 101508, 2022. doi: 10.1016/j.aei.2021.101508.
  32. L. D. Lapitan et al., "An effective blended online teaching and learning strategy during the COVID-19 pandemic," Education for Chemical Engineers, vol. 35, pp. 116–131, 2021. doi: 10.1016/j.ece.2021.01.012.
  33. T. Adams et al., "Patterns in student teachers' learning processes and outcomes of classroom management during their internship," Teaching and Teacher Education, vol. 120, p. 103891, 2022. doi: 10.1016/j.tate.2022.103891.


Download data is not yet available.