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Comparing attrition prediction in FutureLearn and edX MOOCs

Cobos, Ruth, Wilde, Adriana and Zaluska, Ed (2017) Comparing attrition prediction in FutureLearn and edX MOOCs At FutureLearn Workshop in Learning Analytics and Knowledge 2017 (LAK17), Canada. 13 - 17 Mar 2017. 20 pp.

Record type: Conference or Workshop Item (Paper)


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.

PDF FL-LAK-cobos.pdf - Author's Original
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PDF FL-LAK-rc-agw_ejz2 - Accepted Manuscript
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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 (LAK17), Canada, 2017-03-13 - 2017-03-17
Organisations: Web & Internet Science


Local EPrints ID: 405268
PURE UUID: 0b2052f7-b1d2-4cae-89dc-6ab6e85e6fcd

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Date deposited: 02 Feb 2017 11:16
Last modified: 18 Jul 2017 04:06

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Author: Ruth Cobos
Author: Adriana Wilde
Author: Ed Zaluska

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