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Spotting at-risk droppers in MOOCs

TitleSpotting at-risk droppers in MOOCs
Publication TypeConference Proceedings
Year of Publication2018
AuthorsItani, A., Brisson L., & Garlatti S.
Publisher2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT)
Volume00
Pagination223-224
Date Published07/2018
Place PublishedMumbai, India
ISBN2161-377X
Keywordsdecision trees, machine learning, predictive models, radio frequency, testing, trajectory, tuning
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.

URLdoi.ieeecomputersociety.org/10.1109/ICALT.2018.00118
DOI10.1109/ICALT.2018.00118
Rights

Copyright © 2018 IEEE. All rights reserved.

Refereed DesignationRefereed
Total votes: 69