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 SemiMarkov Chains for modeling student's activities and ExpectationMaximization 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. [For the full proceedings, see ED592609.]