Student Engagement Level in an e-Learning Environment: Clustering Using K-Means

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  • Author(s): Moubayed, Abdallah (ORCID Moubayed, Abdallah (ORCID 0000-0002-1476-164X); Injadat, Mohammadnoor (ORCID Injadat, Mohammadnoor (ORCID 0000-0003-1959-0058); Shami, Abdallah (ORCID Shami, Abdallah (ORCID 0000-0003-2887-0350); Lutfiyya, Hanan
  • Language:
    English
  • Source:
    American Journal of Distance Education. 2020 34(2):137-156.
  • Publication Date:
    2020
  • Document Type:
    Journal Articles
    Reports - Research
  • Additional Information
    • Availability:
      Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
    • Peer Reviewed:
      Y
    • Source:
      20
    • Education Level:
      Higher Education
      Postsecondary Education
    • Subject Terms:
      Learner Engagement; Electronic Learning; Individualized Instruction; Undergraduate Students; Learning Analytics; Identification; Interaction; College Science; Foreign Countries; Models; Program Effectiveness; Student Behavior
    • Subject Terms:
      Canada
    • Accession Number:
      10.1080/08923647.2020.1696140
    • ISSN:
      0892-3647
    • Abstract:
      E-learning platforms and processes face several challenges, among which is the idea of personalizing the e-learning experience and to keep students motivated and engaged. This work is part of a larger study that aims to tackle these two challenges using a variety of machine learning techniques. To that end, this paper proposes the use of k-means algorithm to cluster students based on 12 engagement metrics divided into two categories: interaction-related and effort-related. Quantitative analysis is performed to identify the students that are not engaged who may need help. Three different clustering models are considered: two-level, three-level, and five-level. The considered dataset is the students' event log of a second-year undergraduate Science course from a North American university that was given in a blended format. The event log is transformed using MATLAB to generate a new dataset representing the considered metrics. Experimental results' analysis shows that among the considered interaction-related and effort-related metrics, the number of logins and the average duration to submit assignments are the most representative of the students' engagement level. Furthermore, using the silhouette coefficient as a performance metric, it is shown that the two-level model offers the best performance in terms of cluster separation. However, the three-level model has a similar performance while better identifying students with low engagement levels.
    • Abstract:
      As Provided
    • Publication Date:
      2020
    • Accession Number:
      EJ1258775