Predicting potential dropout students in MOOC via data mining

Tak Lam WONG, Siu Cheung KONG, Fu Lee WANG, Reggie KWAN

Research output: Contribution to conferencePaper

Abstract

The emergence of Massive Open Online Courses (MOOC) posted great impact to education. Students could enroll and attend any MOOC anytime and anywhere, depending on their interest, schedule and learning pace. However, the dropout rate of MOOC was known to be high in practice. Earlier identification of students who have high chance to dropout in MOOC is useful for immediate intervention and reduction in dropout rate. In this paper, we aim at applying data mining methods to discover the students who are likely to dropout in MOOC. Real-world data were collected for the evaluation of our proposed method.
Original languageEnglish
Publication statusPublished - May 2015

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drop-out
student
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Citation

Wong, T.-L., Kong, S. C., Wang, F. L., & Kwan, R. (2015, May). Predicting potential dropout students in MOOC via data mining. Paper presented at The 19th Global Chinese Conference on Computers in Education (GCCCE 2015), Howard Civil Service International House, Taiwan.

Keywords

  • MOOC
  • Data mining
  • Dropout rate
  • Decision tree