Predicting student attrition in MOOCs using sentiment analysis and neural networks

Devendra Singh Chaplot, Eunhee Rhim, Jihie Kim

Research output: Contribution to journalConference articlepeer-review

69 Scopus citations

Abstract

While there is increase in popularity of massive open online courses in recent years, high rates of drop-out in these courses makes predicting student attrition an important problem to solve. In this paper, we propose an algorithm based on artificial neural network for predicting student attrition in MOOCs using sentiment analysis and show the significance of student sentiments in this task. To the best of our knowledge, use of user sentiments and neural networks for this task is novel and our algorithm beats the state-of-the-art algorithm on this task in terms of Cohen's kappa.

Original languageEnglish
Pages (from-to)7-12
Number of pages6
JournalCEUR Workshop Proceedings
Volume1432
StatePublished - 2015
EventWorkshops at the 17th International Conference on Artificial Intelligence in Education, AIED-WS 2015 - Madrid, Spain
Duration: 22 Jun 201526 Jun 2015

Keywords

  • Educational data mining
  • MOOC
  • Neural network
  • Sentiment analysis
  • Student attrition

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