Investigating demographics and behavioral engagement associated with online learning performance

Yicong LIANG, Di ZOU, Fu Lee WANG, Haoran XIE, Simon K. S. CHEUNG

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

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 languageEnglish
Title of host publicationBlended learning: Lessons learned and ways forward: 16th International Conference on Blended Learning, ICBL 2023, Proceedings
EditorsChen LI, Simon K. S. CHEUNG, Fu Lee WANG, Angel LU, Lam For KWOK
PublisherSpringer
Pages124-136
ISBN (Electronic)9783031357312
ISBN (Print)9783031357305
DOIs
Publication statusPublished - 2023

Citation

Liang, Y., Zou, D., Wang, F. L., Xie, H., & Cheung, S. K. S. (2023). Investigating demographics and behavioral engagement associated with online learning performance. In C. Li, S. K. S. Cheung, F. L. Wang, A. Lu, & L. F. Kwok (Eds.), Blended learning: Lessons learned and ways forward: 16th International Conference on Blended Learning, ICBL 2023, Proceedings (pp. 124-136). Springer. https://doi.org/10.1007/978-3-031-35731-2_12

Keywords

  • Educational data analysis
  • Online learning performance
  • OULAD dataset
  • Virtual learning environment

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