Investigating Demographics and Behavioral Engagement Associated with Online Learning Performance

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

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

1 Citation (Scopus)


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.
Original languageEnglish
Title of host publicationBlended Learning
Subtitle of host publicationLessons 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 Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Electronic)9783031357312
ISBN (Print)9783031357305
Publication statusPublished - Jul 2023
Event16th International Conference on Blended Learning, ICBL 2023 - Hong Kong, China
Duration: 17 Jul 202320 Jul 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13978 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Blended Learning, ICBL 2023
Abbreviated titleICBL2023
CityHong Kong

Bibliographical note

Funding Information:
Xie’s work has been supported by the Direct Grant (DR23B2) and the Faculty Research Grant (DB23A3) of Lingnan University, Hong Kong.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Educational Data Analysis
  • Online Learning Performance
  • OULAD dataset
  • Virtual Learning Environment


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