Predicting attrition from massive open online courses in FutureLearn and edX
Predicting attrition from massive open online courses in FutureLearn and edX
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.
Attribute selection, Attrition, EdX, FutureLearn, Learning analytics, MOOCs, Prediction
Cobos, Ruth
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Wilde, Adriana
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Zaluska, Ed
6f88ee05-711d-412b-974a-6afe64ec3e71
2017
Cobos, Ruth
1e88b8a4-ef1b-4716-ad48-fb776a7b2eba
Wilde, Adriana
4f9174fe-482a-4114-8e81-79b835946224
Zaluska, Ed
6f88ee05-711d-412b-974a-6afe64ec3e71
Cobos, Ruth, Wilde, Adriana and Zaluska, Ed
(2017)
Predicting attrition from massive open online courses in FutureLearn and edX.
In CEUR Workshop Proceedings (2017).
vol. 1967,
20 pp
.
Record type:
Conference or Workshop Item
(Paper)
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.
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Published date: 2017
Keywords:
Attribute selection, Attrition, EdX, FutureLearn, Learning analytics, MOOCs, Prediction
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Local EPrints ID: 436290
URI: http://eprints.soton.ac.uk/id/eprint/436290
PURE UUID: c8f5cd8f-57c5-4d6e-8071-73d1be324981
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Date deposited: 06 Dec 2019 17:30
Last modified: 17 Mar 2024 03:23
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Contributors
Author:
Ruth Cobos
Author:
Adriana Wilde
Author:
Ed Zaluska
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