Abstract
Online courses in higher education have gained popularity, but students struggle with self-regulation in online learning. The absence of traditional classroom guidance due to limited educator oversight highlights the need for effective student profiling. Existing profiling methods focus on non-university contexts with data-rich platforms, leaving platforms like Moodle at a disadvantage. In this paper, we explore the creation of useful student profiles with limited data, often found in Moodle and similar platforms. We propose to adopt a clustering method based on eight key self-regulation variables: revision, progress, consistency, dedication, regularity, focus, and practicality. Across diverse online university courses, our experiments show that our approach effectively identifies meaningful profiles, even with limited data. These profiles also reveal unique demographics, providing insights into online learning behavior.
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Notes
- 1.
SCORM stands for Sharable Content Object Reference Model, and it is a set of standards and specifications for creating and packaging e-learning content. A SCORM package typically includes a collection of web-based learning resources (such as presentations, quizzes, and simulations) that adhere to the SCORM standards.
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Fenu, G., Galici, R., Marras, M. (2024). Data-Efficient Student Profiling in Online Courses. In: Palomba, F., Gravino, C. (eds) Artificial Intelligence with and for Learning Sciences. Past, Present, and Future Horizons. WAILS 2024. Lecture Notes in Computer Science, vol 14545. Springer, Cham. https://doi.org/10.1007/978-3-031-57402-3_2
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