@proceedings {
	title = {Spotting at-risk droppers in MOOCs},
	author = {Itani, Alya and Brisson, Laurent and Garlatti, Serge},
	abstract = {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.},
	year = {2018},
	month = {07/2018},
	publisher = {IEEE},
	volume = {00},
	pages = {223-224},
	address = {Mumbai},
	country = {France},
	doi = {10.1007/978-3-030-03493-1_25},
	url = {doi.ieeecomputersociety.org/10.1109/ICALT.2018.00118},
	issn = {2161-377X},
	refereed = {yes},
	keywords = {decision trees, machine learning, predictive models, radio frequency, testing, trajectory, tuning},
}