Faucon, Louis
Kidzinski, Lukasz
Dillenbourg, Pierre
Semi-Markov model for simulating MOOC students
Proceedings of the 9th International Conference on Educational Data Mining
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.
MOOCs;
simulation of students;
generative models;
Expectation-Maximization;
Semi-Markov chains;
Bayesian statistics;
2016
http://infoscience.epfl.ch/record/218881/files/EDM16___simulations.pdf;