Predicting MOOC Dropout Based on Learner’s Activity Features

  • Authors

    • Soufiane Ardchir
    • Mohamed Amine Talhaoui
    • Houda Jihal
    • Mohamed Azzouazi
    2018-12-06
    https://doi.org/10.14419/ijet.v7i4.32.25360
  • MOOCs, Dropout prediction, Big data, Machine learning.

  • Abstract

    Over the last few years, open massive online courses (MOOC) have become very popular and greatly enhanced as they present a way of learning mostly free online used around the world by millions of participants. Despite all the characteristics and benefits of MOOC, however, one of the crucial problems associated with MOOC is their high dropout rate (completion rate below 13%), which questions the effectiveness of learning technology. The analysis of MOOC data provides a useful means of identifying characteristics that can help to understand the behavior of the learners and to accompany them in order to succeed in their learning. In this paper, we present a dropout predictor that uses student activity features based on machine learning methods for identification of students who are at risk of not completing courses.

     

     


     

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  • How to Cite

    Ardchir, S., Amine Talhaoui, M., Jihal, H., & Azzouazi, M. (2018). Predicting MOOC Dropout Based on Learner’s Activity Features. International Journal of Engineering & Technology, 7(4.32), 124-126. https://doi.org/10.14419/ijet.v7i4.32.25360

    Received date: 2019-01-04

    Accepted date: 2019-01-04

    Published date: 2018-12-06