Predicting attrition from massive open online courses in FutureLearn and edX

Ruth Cobos, Adriana Wilde, Ed Zaluska

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationJoint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017; Vancouver; Canada; 13 March 2017 through 17 March 2017
EditorsLorenzo Vigentini, Yuan Wang, Luc Paquette, Manuel León Urrutia
PublisherSun SITE Central Europe
Pages74-93
Number of pages20
Publication statusPublished - 2017
EventJoint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017 - Vancouver, Canada
Duration: 13 Mar 201717 Mar 2017
http://lak17.solaresearch.org/

Publication series

NameCEUR Workshop Proceedings
Volume1967
ISSN (Print)1613-0073

Conference

ConferenceJoint MOOCs Workshops from the Learning Analytics and Knowledge Conference, LAK 2017
Abbreviated titleLAK 2017
Country/TerritoryCanada
CityVancouver
Period13/03/1717/03/17
Internet address

Keywords

  • Attribute selection
  • Attrition
  • EdX
  • FutureLearn
  • Learning analytics
  • MOOCs
  • Prediction

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