%0	Conference Proceedings
%A	Itani, Alya
%A	Brisson, Laurent
%A	Garlatti, Serge
%C	Mumbai, France, Europe
%D	2018
%I	IEEE
%K	decision trees
%K	machine learning
%K	predictive models
%K	radio frequency
%K	testing
%K	trajectory
%K	tuning
%P	223-224
%R	10.1007/978-3-030-03493-1_25
%T	Spotting at-risk droppers in MOOCs
%U	doi.ieeecomputersociety.org/10.1109/ICALT.2018.00118
%V	00
%X	In this paper, we propose a machine learning based drop-out prediction process for spotting and preventing drop-out among MOOC learners early upon their interaction with the course. Two main goals are perused in this scope, first, achieving an accurate prediction at a specified instant of the course, established with the help of predictive type classifiers. Second, uncovering the underlying reasons for the predicted drop-out established with the help of explicative type classifiers. The related experimental findings show promising results.
%8	07/2018
%@	2161-377X
%*	yes