Kassim, M. S. S. and Azizul, Z. H. and Ahmad Fuaad, A. A. H. (2025) Student Engagement Dataset (SED): An Online Learning Activity Dataset. IEEE Access, 13. pp. 23607-23617. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2025.3531102.
Full text not available from this repository.Abstract
Distance learning has become a popular educational medium, and the Internet has spread since the early 2000s. To leverage this phenomenon, learning analytics and data mining can provide insights into improving pedagogy and assessing student engagement. To this end, a student-centric dataset was constructed by extracting data from Universiti Malaya's Moodle-based Virtual Learning Environment (VLE), which serves approximately 25,000 students annually. In this paper, we present the Student Engagement Dataset (SED). The dataset consists of 16,609 students and 2,407 courses. It contains information such as grades and daily logged online activities (approximately 12 million data points), including temporal data across four tables. The tables include student engagement features created by aggregating raw activity data. Here, we present the dataset's properties and describe the data collection, selection, and processing steps. Correlation analysis of student engagement features showed a statistically significant but weak negative correlation between the number of courses, early morning logins, assignments, and top students' performance. SED is expected to present new opportunities for researchers in the learning analytics domain.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya (GPF009D-2019) |
| Uncontrolled Keywords: | Learning analytics; learning management systems (LMSs); online learning; online learning; virtual learning environments (VLEs); virtual learning environments (VLEs); student engagement; student engagement; student engagement |
| Subjects: | L Education > LC Special aspects of education Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence Faculty of Science > Department of Chemistry |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 May 2025 07:46 |
| Last Modified: | 14 May 2025 07:46 |
| URI: | http://eprints.um.edu.my/id/eprint/48033 |
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