Data-Efficient Student Profiling in Online Courses

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.