000218881 001__ 218881
000218881 005__ 20190416220331.0
000218881 037__ $$aCONF
000218881 245__ $$aSemi-Markov model for simulating MOOC students
000218881 269__ $$a2016
000218881 260__ $$c2016
000218881 336__ $$aConference Papers
000218881 520__ $$aLarge-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.
000218881 6531_ $$aMOOCs
000218881 6531_ $$asimulation of students
000218881 6531_ $$agenerative models
000218881 6531_ $$aExpectation-Maximization
000218881 6531_ $$aSemi-Markov chains
000218881 6531_ $$aBayesian statistics
000218881 700__ $$aFaucon, Louis
000218881 700__ $$0248433$$g251561$$aKidzinski, Lukasz
000218881 700__ $$0240137$$g155704$$aDillenbourg, Pierre
000218881 7112_ $$dJune 30 - July 2, 2016$$cRaleigh, USA$$a9th International Conference on Educational Data Mining
000218881 773__ $$tProceedings of the 9th International Conference on Educational Data Mining
000218881 8564_ $$uhttps://infoscience.epfl.ch/record/218881/files/EDM16___simulations.pdf$$zPreprint$$s458300$$yPreprint
000218881 909C0 $$xU12753$$0252475$$pCHILI
000218881 909CO $$ooai:infoscience.tind.io:218881$$qGLOBAL_SET$$pconf$$pIC
000218881 917Z8 $$x251561
000218881 937__ $$aEPFL-CONF-218881
000218881 973__ $$rREVIEWED$$sACCEPTED$$aEPFL
000218881 980__ $$aCONF