Predicting Students' Continuance Use of Learning Management System at a Technical University Using Machine Learning Algorithms

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  • Additional Information
    • Availability:
      Emerald Publishing Limited. Howard House, Wagon Lane, Bingley, West Yorkshire, BD16 1WA, UK. Tel: +44-1274-777700; Fax: +44-1274-785201; e-mail: [email protected]; Web site: http://www.emerald.com/insight
    • Peer Reviewed:
      Y
    • Source:
      19
    • Education Level:
      Higher Education
      Postsecondary Education
    • Subject Terms:
      Foreign Countries; College Students; Learning Management Systems; Student Behavior; Predictor Variables; Artificial Intelligence; Algorithms; Electronic Learning; Models
    • Subject Terms:
      Ghana
    • Accession Number:
      10.1108/ITSE-11-2021-0202
    • ISSN:
      1741-5659
      1758-8510
    • Abstract:
      Purpose: This study aims to investigate factors that could predict the continued usage of e-learning systems, such as the learning management systems (LMS) at a Technical University in Ghana using machine learning algorithms. Design/methodology/approach: The proposed model for this study adopted a unified theory of acceptance and use of technology as a base model and incorporated the following constructs: availability of resources (AR), computer self-efficacy (CSE), perceived enjoyment (PE) and continuance intention to use (CIU). The study used an online questionnaire to collect data from 280 students of a Technical University in Ghana. The partial least square-structural equation model (PLS-SEM) method was used to determine the measurement model's reliability and validity. Machine learning algorithms were used to determine the relationships among the constructs in the proposed research model. Findings: The findings from the study confirmed that AR, CSE, PE, performance expectancy, effort expectancy and social influence predicted students' continuance intention to use the LMS. In addition, CIU and facilitating conditions predicted the continuance use of the LMS. Originality/value: The use of machine learning algorithms in e-learning systems literature has been rarely used. Thus, this study contributes to the literature on the continuance use of e-learning systems using machine learning algorithms. Furthermore, this study contributes to the literature on the continuance use of e-learning systems in developing countries, especially in a Ghanaian higher education context.
    • Abstract:
      As Provided
    • Publication Date:
      2023
    • Accession Number:
      EJ1376693