Dropout Prediction in MOOCs: A Comparison Between Process and Sequence Mining

Author:

Deeva Galina,De Smedt Johannes,De Koninck Pieter,De Weerdt Jochen

Publisher

Springer International Publishing

Reference29 articles.

1. Milligan, C., Margaryan, A., Littlejohn, A.: Patterns of engagement in massive open online courses. J. Online Learn. Technol. 9(2), 149–159 (2013)

2. Zheng, S., Rosson, M.B., Shih, P.C., Carroll, J.M.: Understanding student motivation, behaviors and perceptions in MOOCs. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1882–1895. ACM (2015)

3. Eriksson, T., Adawi, T., Stöhr, C.: “Time is the bottleneck”: a qualitative study exploring why learners drop out of MOOCs. J. Comput. High. Educ. 29(1), 133–146 (2017)

4. Yang, D., Sinha, T., Adamson, D., Rosé, C.P.: Turn on, tune in, drop out: anticipating student dropouts in massive open online courses. In: Proceedings of the 2013 NIPS Data-driven Education Workshop. vol. 11, p. 14 (2013)

5. Adamopoulos, P.: What makes a great MOOC? An interdisciplinary analysis of student retention in online courses (2013)

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