Semi-Markov model for simulating MOOC students

Large-scale experiments are often expensive and time consuming. Although Massive Online Open Courses (MOOCs) provide a solid and consistent framework for learning analytics, MOOC practitioners are still reluctant to risk resources in experiments. In this study, we suggest a methodology for simulating MOOC students, which allow estimation of distributions, before implementing a large-scale experiment. To this end, we employ generative models to draw independent samples of artificial students in Monte Carlo simulations. We use Semi-Markov Chains for modeling student's activities and Expectation-Maximization algorithm for fitting the model. From the fitted model, we generate simulated students whose processes of weekly activities are similar to these of the real students.


Published in:
Proceedings of the 9th International Conference on Educational Data Mining
Presented at:
9th International Conference on Educational Data Mining, Raleigh, USA, June 30 - July 2, 2016
Year:
2016
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 Record created 2016-06-21, last modified 2018-12-03

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