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High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs

Author

Listed:
  • Dries Benoit

    (UGENT - Universiteit Gent = Ghent University = Université de Gand)

  • Wai Kit Tsang

    (UGENT - Universiteit Gent = Ghent University = Université de Gand)

  • Kristof Coussement

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Annelies Raes

    (KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven)

Abstract

In this study, we analyze the learning behavior of 24,000 students in a fully asynchronous subscription-based MOOC platform using hidden Markov models (HMMs) to examine the relationship between learning motivation and student drop-out behavior. In contrast to previous findings, our results reveal that student drop-out is not necessarily correlated with low motivation, as students may drop out despite being highly motivated at the end of their learning journey. To design more effective student retention campaigns, educational decision-makers must consider the motivation level and target potential drop-outs with a low state of motivation. More specifically, our findings emphasize the need for early intervention to prevent students from dropping out, as it becomes challenging to stimulate motivation once it reaches its lowest state. By adopting our proposed methodology, decision-makers can gain a better understanding of the student drop-out process and make more informed student retention interventions.

Suggested Citation

  • Dries Benoit & Wai Kit Tsang & Kristof Coussement & Annelies Raes, 2024. "High-stake student drop-out prediction using hidden Markov models in fully asynchronous subscription-based MOOCs," Post-Print hal-04542480, HAL.
    • Handle: RePEc:hal:journl:hal-04542480
    DOI: 10.1016/j.techfore.2023.123009
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