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Dropout Prediction in MOOCs: A Comparison Between Process and Sequence Mining

  • Galina Deeva
  • Johannes De Smedt
  • Pieter De Koninck
  • Jochen De Weerdt
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 308)

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.

Keywords

Dropout prediction Process mining Sequence classification Massive Open Online Course Educational data mining 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Galina Deeva
    • 1
  • Johannes De Smedt
    • 2
  • Pieter De Koninck
    • 1
  • Jochen De Weerdt
    • 1
  1. 1.Department of Decision Sciences and Information Management, Faculty of Economics and BusinessKU LeuvenLeuvenBelgium
  2. 2.Management Science and Business Economics Group, Business SchoolUniversity of EdinburghEdinburghScotland

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