Abstract
Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence.
Original language | English |
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Title of host publication | 2017 IEEE 29TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2017) |
Publisher | IEEE |
Pages | 231-238 |
Number of pages | 8 |
ISBN (Print) | 9781538638767 |
DOIs | |
Publication status | Published - Nov 2017 |
Event | 29th Annual IEEE International Conference on Tools with Artificial Intelligence - MA, Boston, United States Duration: 6 Nov 2017 → 8 Nov 2017 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=63005©ownerid=89409 |
Publication series
Series | International Conference on Tools With Artificial Intelligence Proceedings |
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ISSN | 1082-3409 |
Conference
Conference | 29th Annual IEEE International Conference on Tools with Artificial Intelligence |
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Abbreviated title | ICTAI |
Country/Territory | United States |
City | Boston |
Period | 6/11/17 → 8/11/17 |
Internet address |
Keywords
- Massive Open Online Courses
- Imbalanced Classification
- Temporal Dropout Prediction
- RANDOM FOREST
- CLASSIFICATION