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Investigating Demographics and Behavioral Engagement Associated with Online Learning Performance

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13978)


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.


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

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  1. 1.

  2. 2.

    Four types of final result: withdrawn, fail, pass, distinction.

  3. 3.

    Seven modules (A-G) are contained in OULAD.

  4. 4.

    STEM: Science, technology, engineering, and mathematics.

  5. 5.

    The number of different days for a student interacted with VLE during her presentation.

  6. 6.

    Resource activity type refers to a segment of text the student is supposed to read, forum points to forum space of the course.


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

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Correspondence to Yicong Liang .

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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: Li, C., Cheung, S.K.S., Wang, F.L., Lu, A., Kwok, L.F. (eds) Blended Learning : Lessons Learned and Ways Forward . ICBL 2023. Lecture Notes in Computer Science, vol 13978. Springer, Cham.

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  • Print ISBN: 978-3-031-35730-5

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