In recent years, online learning has become a viable alternative for learners worldwide to pursue higher education and gain advanced technical skills. In this work, we focused on data analysis to scrutinize the features associated with online learning performance and course selection. In particular, we investigated and compared how student demographic characteristics and behavioral engagement associated with academic performance based on a publicly accessible Open University Learning Analytics dataset (OULAD). We find that neighborhood poverty level, education background, active learning days and interaction times are positively associated with final learning results. In addition, students with different genders had bias in online course selection, where female students tended to favor social science courses and male had a preference for STEM. Students who performed well mainly came from learners with a well-educated prior background.
|Title of host publication||Blended Learning|
|Subtitle of host publication||Lessons Learned and Ways Forward - 16th International Conference on Blended Learning, ICBL 2023, Proceedings|
|Editors||Chen LI, Simon K. S. CHEUNG, Fu Lee WANG, Angel LU, Lam For KWOK|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||13|
|Publication status||Published - Jul 2023|
|Event||16th International Conference on Blended Learning, ICBL 2023 - Hong Kong, China|
Duration: 17 Jul 2023 → 20 Jul 2023
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th International Conference on Blended Learning, ICBL 2023|
|Period||17/07/23 → 20/07/23|
Bibliographical noteFunding Information:
Xie’s work has been supported by the Direct Grant (DR23B2) and the Faculty Research Grant (DB23A3) of Lingnan University, Hong Kong.
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Educational Data Analysis
- Online Learning Performance
- OULAD dataset
- Virtual Learning Environment