Spotting at-risk droppers in MOOCs
Published | July 2018 |
Conference | 2018 IEEE 18th International Conference on Advanced Learning Technologies (ICALT) Volume 00, Pages 223-224 |
Publisher | IEEE |
Country | France, Europe |
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.Keywords | decision trees · machine learning · predictive models · radio frequency · testing · trajectory · tuning |
Published at | Mumbai |
ISSN | 2161-377X |
Refereed | Yes |
Rights | Copyright © 2018 IEEE. All rights reserved. |
DOI | 10.1007/978-3-030-03493-1_25 |
URL | http://doi.ieeecomputersociety.org/10.1109/ICALT.2018.00118 |
Export options | BibTex · EndNote · Tagged XML · Google Scholar |
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