The University of Southampton
University of Southampton Institutional Repository

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
37ee0dec-a07f-4177-b291-96037fe48e14
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 At FutureLearn Workshop in Learning Analytics and Knowledge 2017, Vancouver, 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.

Text FL-LAK-cobos.pdf - Author's Original
Restricted to Repository staff only until 11 March 2017.
Text FL-LAK-rc-agw_ejz2 - Accepted Manuscript
Download (368kB)

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, Vancouver, 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

Catalogue record

Date deposited: 02 Feb 2017 11:16
Last modified: 21 Nov 2017 17:31

Export record

Contributors

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

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×