Abstract
There are a number of similarities and differences between Future-Learn MOOCs and those offered by other platforms, such as edX. In this research we compare the results of applying machine learning algorithms to predict course attrition for two case studies using datasets from a selected Future-Learn MOOC and an edX MOOC of comparable structure and themes. For each we have computed a number of attributes in a pre-processing stage from the raw data available in each course. Following this, we applied several machine learning algorithms on the pre-processed data to predict attrition levels for each course. The analysis suggests that the attribute selection varies in each scenario, which also impacts on the behaviour of the predicting algorithms.
Original language | English |
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Title of host publication | Joint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017; Vancouver; Canada; 13 March 2017 through 17 March 2017 |
Editors | Lorenzo Vigentini, Yuan Wang, Luc Paquette, Manuel León Urrutia |
Publisher | Sun SITE Central Europe |
Pages | 74-93 |
Number of pages | 20 |
Publication status | Published - 2017 |
Event | Joint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017 - Vancouver, Canada Duration: 13 Mar 2017 → 17 Mar 2017 http://lak17.solaresearch.org/ |
Publication series
Name | CEUR Workshop Proceedings |
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Volume | 1967 |
ISSN (Print) | 1613-0073 |
Conference
Conference | Joint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017 |
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Abbreviated title | LAK 2017 |
Country/Territory | Canada |
City | Vancouver |
Period | 13/03/17 → 17/03/17 |
Internet address |
Keywords
- Attribute selection
- Attrition
- EdX
- FutureLearn
- Learning analytics
- MOOCs
- Prediction