Faucon, LouisKidzinski, LukaszDillenbourg, Pierre2016-06-212016-06-212016-06-212016https://infoscience.epfl.ch/handle/20.500.14299/126774Large-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.MOOCssimulation of studentsgenerative modelsExpectation-MaximizationSemi-Markov chainsBayesian statisticstext::conference output::conference proceedings::conference paper