Predicting Student Attrition in MOOCs using Sentiment Analysis and Neural Networks

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.

• Type: Journal
• Year: 2015
• Where: AIED
• Authors: Devendra Singh Chaplot, Eunhee Rhim, Jihie Kim
• Keywords: AIED 2015, Artificial Intelligence