Predicting attrition from massive open online courses in FutureLearn and edX

ISSN: 16130073
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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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Cobos, R., Wilde, A., & Zaluska, E. (2017). Predicting attrition from massive open online courses in FutureLearn and edX. In CEUR Workshop Proceedings (Vol. 1967, pp. 74–93). CEUR-WS.

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