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Predicting attrition from massive open online courses in FutureLearn and edX

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. Copyright © 2017 for the individual papers by the papers' authors.
Attribute selection, Attrition, EdX, FutureLearn, Learning analytics, MOOCs, Prediction
Cobos, Ruth
1e88b8a4-ef1b-4716-ad48-fb776a7b2eba
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Zaluska, Ed
6f88ee05-711d-412b-974a-6afe64ec3e71
Cobos, Ruth
1e88b8a4-ef1b-4716-ad48-fb776a7b2eba
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
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. Copyright © 2017 for the individual papers by the papers' authors.

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More information

Published date: 2017
Keywords: Attribute selection, Attrition, EdX, FutureLearn, Learning analytics, MOOCs, Prediction

Identifiers

Local EPrints ID: 436290
URI: http://eprints.soton.ac.uk/id/eprint/436290
PURE UUID: c8f5cd8f-57c5-4d6e-8071-73d1be324981
ORCID for Adriana Wilde: ORCID iD orcid.org/0000-0002-1684-1539

Catalogue record

Date deposited: 06 Dec 2019 17:30
Last modified: 17 Dec 2019 01:32

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