Promoting University Students’ Self-Regulated Learning Skills on E-Learning Platforms Using Educational Data Mining
| dc.contributor.author | Araka, Eric | |
| dc.contributor.author | Wario, Ruth | |
| dc.contributor.author | Maina, Elizaphan | |
| dc.date.accessioned | 2025-09-30T12:50:07Z | |
| dc.date.available | 2025-09-30T12:50:07Z | |
| dc.date.issued | 2025 | |
| dc.description | Research Paper | |
| dc.description.abstract | Current e-learning platforms used by higher education institutions for open and distance learning often lack automation tools that offer personalized support to students. As a result, learners must take an active role in managing their learning process, as emphasized by the self-regulated learning theory. Given the flexibility of online learning, where students are not bound by fixed schedules, they must develop self-discipline and autonomy to effectively navigate and control their learning experience. This study examines the potential of educational data mining techniques to enhance students' self-regulated learning skills. A true experimental research design was deployed to assess the effectiveness of the developed educational data mining techniques -based interventions in fostering self-regulated learning. The study sample consisted of University students enrolled in a 12-week Data Science with Python course. Results from an independent t-test indicated that educational data mining interventions positively influenced students’ self-regulatory skills by strengthening their cognitive and behavioral learning strategies. However, no significant difference was found in final academic performance between students who received educational data mining interventions and those who received instructor-led support. By providing data-driven support, the study contributes to efforts aimed at improving student retention, reducing dropout rates, and enhancing academic performance in online learning environments. These findings provide a foundation for future research on integrating intelligent automation to support SRL in higher education. | |
| dc.description.sponsorship | Kenya Education Network (KENET) under Grant number KENET/EDTECH/2924/1 | |
| dc.identifier.citation | Kituku, B., Araka, E., & Muuro, E. (2025, May). Integrating Generative Artificial Intelligence in Assessment Generation for Higher Education: Computer Science Use Case. In 2025 IST-Africa Conference (IST-Africa) (pp. 1-10). IEEE. | |
| dc.identifier.isbn | 978-1-905824-75-5 | |
| dc.identifier.uri | https://ir-library.ku.ac.ke/handle/123456789/31480 | |
| dc.language.iso | en | |
| dc.publisher | IST-Africa | |
| dc.title | Promoting University Students’ Self-Regulated Learning Skills on E-Learning Platforms Using Educational Data Mining | |
| dc.type | Article |