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Comparing attrition prediction in FutureLearn and edX MOOCs

Comparing attrition prediction in FutureLearn and edX MOOCs
Comparing attrition prediction in FutureLearn and edX MOOCs
There are a number of similarities and differences between FutureLearn 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 FutureLearn 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.
Cobos, Ruth
1e88b8a4-ef1b-4716-ad48-fb776a7b2eba
Wilde, Adriana
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Zaluska, Ed
43f6a989-9542-497e-bc9d-fe20f03cad35
Cobos, Ruth
1e88b8a4-ef1b-4716-ad48-fb776a7b2eba
Wilde, Adriana
37ee0dec-a07f-4177-b291-96037fe48e14
Zaluska, Ed
43f6a989-9542-497e-bc9d-fe20f03cad35

Cobos, Ruth, Wilde, Adriana and Zaluska, Ed (2017) Comparing attrition prediction in FutureLearn and edX MOOCs. FutureLearn Workshop in Learning Analytics and Knowledge 2017, Canada. 13 - 17 Mar 2017. 20 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

There are a number of similarities and differences between FutureLearn 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 FutureLearn 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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More information

Accepted/In Press date: 20 January 2017
e-pub ahead of print date: March 2017
Venue - Dates: FutureLearn Workshop in Learning Analytics and Knowledge 2017, Canada, 2017-03-13 - 2017-03-17
Organisations: Web & Internet Science

Identifiers

Local EPrints ID: 405268
URI: http://eprints.soton.ac.uk/id/eprint/405268
PURE UUID: 0b2052f7-b1d2-4cae-89dc-6ab6e85e6fcd
ORCID for Adriana Wilde: ORCID iD orcid.org/0000-0002-1684-1539

Catalogue record

Date deposited: 02 Feb 2017 11:16
Last modified: 22 Jul 2020 04:01

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