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.
  • 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.

     

     


     
  • References

    1. [1] J. E. Luaran, “Massive Open Online Course (MOOC),†p. 165, 2013.

      [2] K. Jordan, “Massive Open Online Course Completion Rates Revisited: Assessment, Length a...: EBSCOhost,†vol. 16, no. 3, pp. 341–358, 2015.

      [3] S. L. Miller, “Teaching an Online Pedagogy MOOC,†MERLOT J. Online Learn. Teach., vol. 11, no. 1, pp. 104–119, 2015.

      [4] S. Ardchir., M.A.Talhaoui, M.Azzouazi (2017) Towards an Adaptive Learning Framework for MOOCs. In: Aïmeur E., Ruhi U., Weiss M. (eds) E-Technologies: Embracing the Internet of Things. MCETECH 2017. Lecture Notes in Business Information Processing, vol 289. Springer, Cham

      [5] C. Gütl, R. H. Rizzardini, V. Chang, and M. Morales, “Attrition in MOOC: Lessons Learned from Drop-Out Students,†Commun. Comput. Inf. Sci., vol. 446 CCIS, pp. 37–48, 2014.

      [6] A. Margaryan, M. Bianco, and A. Littlejohn, “Instructional quality of Massive Open Online Courses (MOOCs),†Comput. Educ., vol. 80, pp. 77–83, 2015.

      [7] D. Clow, “MOOCs and the funnel of participation,†Proc. Third Int. Conf. Learn. Anal. Knowl. - LAK ’13, p. 185, 2013.

      [8] R. F. Kizilcec, C. Piech, and E. Schneider, “Deconstructing Disengagement : Analyzing Learner Subpopulations in Massive Open Online Courses,†Lak ’13, p. 10, 2013.

      [9] M. Kloft, F. Stiehler, Z. Zheng, and N. Pinkwart, “Predicting MOOC Dropout over Weeks Using Machine Learning Methods,†Proc. 2014 Conf. Empir. Methods Nat. Lang. Process., pp. 60–65, 2014.

      [10] S. Halawa, D. Greene, and J. Mitchell, “Dropout Prediction in MOOCs using Learner Activity Features,†eLearning Pap., vol. 37, no. March, pp. 1–10, 2014.

      [11] G. Balakrishnan and D. Coetzee, “Predicting student retention in massive open online courses using hidden markov models,†Electr. Eng., 2013.

      [12] W. Xing, X. Chen, J. Stein, and M. Marcinkowski, “Erratum: Corrigendum to ‘Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization’ (Computers in Human Behavior (2016) 58 (119–129) (S074756321530279X)(10.1016/j.chb.2015.12.007)) ,†Comput. Human Behav., vol. 66, p. 409, 2017.

      [13] A. L’Heureux, K. Grolinger, H. F. Elyamany, and M. A. M. Capretz, “Machine Learning With Big Data: Challenges and Approaches,†IEEE Access, vol. 5, pp. 7776–7797, 2017.

      [14] KDD cup 2015. The website may be down already. https://kddcup2015.com/

      [15] C. Cortes and V. Vapnik, “Support-Vector Networks,†Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.

  • Downloads

  • 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