Dropout prediction in MOOCs: A comparison between process and sequence mining

Galina Deeva, J. De Smedt, P. De Koninck, Jochen De Weerdt

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

Recently, Massive Open Online Courses (MOOCs) have experienced rapid development. However, one of the major issues of online education is the high dropout rates of participants. Many studies have attempted to explore this issue, using quantitative and qualitative methods for student attrition analysis. Nevertheless, there is a lack of studies which (1) predict the actual moment of dropout, providing opportunities to enhance MOOCs’ student retention by offering timely interventions; and (2) compare the performance of such predicting algorithms. In this paper, we aim to predict student drop out in MOOCs using process and sequence mining techniques, and provide a comparative analysis of these techniques. We perform a case study based on the data from KU Leuven online course “Trends in e-Psychology”, available on the edX platform. The results reveal, that while process mining is better capable to perform descriptive analysis, sequence mining techniques provide better features for predictive purposes. © Springer International Publishing AG 2018.
Original languageEnglish
Title of host publicationBusiness Process Management Workshops
Subtitle of host publicationBPM 2017International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers
EditorsErnest Teniente, Matthias Weidlich
PublisherSpringer
Pages243-255
Number of pages13
ISBN (Electronic)978-3-319-74030-0
ISBN (Print)978-3-319-74029-4
DOIs
Publication statusPublished - 17 Jan 2018

Publication series

NameLecture Notes in Business Information Processing
Volume308
ISSN (Print)1865-1348
ISSN (Electronic)1865-1356

Keywords / Materials (for Non-textual outputs)

  • dropout prediction
  • educational data mining
  • massive open online course
  • process mining
  • sequence classification

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