Abstract
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. Copyright © 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG.
| Original language | English |
|---|---|
| Title of host publication | Proceeding of Blended learning: Lessons learned and ways forward: 16th International Conference on Blended Learning, ICBL 2023 |
| Editors | Chen LI, Simon K. S. CHEUNG, Fu Lee WANG, Angel LU, Lam For KWOK |
| Place of Publication | Cham, Switzerland |
| Publisher | Springer |
| Pages | 124-136 |
| ISBN (Electronic) | 9783031357312 |
| ISBN (Print) | 9783031357305 |
| DOIs | |
| Publication status | Published - 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 4 Quality Education
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SDG 5 Gender Equality
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SDG 11 Sustainable Cities and Communities
Keywords
- Educational data analysis
- Online learning performance
- OULAD dataset
- Virtual learning environment
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