Temporal Models for Predicting Student Dropout in Massive Open Online Courses

• Published in: IEEE ... International Conference on Data Mining workshops pp. 256 - 263
• Main Authors: Fei, Mi, Yeung, Dit-Yan
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.11.2015
• Subjects: Computer assisted instruction; Distance education; Dropouts; Hidden Markov models; Labeling; Lectures; Logistics; Mathematical models; Online; Platforms; Predictive models; Recurrent neural networks; Student activities; Students; Support vector machines
• ISSN: 2375-9259
• DOI: 10.1109/ICDMW.2015.174

Over the past few years, the rapid emergence of massive open online courses (MOOCs) has sparked a great deal of research interest in MOOC data analytics. Dropout prediction, or identifying students at risk of dropping out of a course, is an important problem to study due to the high attrition rate commonly found on many MOOC platforms. The methods proposed recently for dropout prediction apply relatively simple machine learning methods like support vector machines and logistic regression, using features that reflect such student activities as lecture video watching and forum activities on a MOOC platform during the study period of a course. Since the features are captured continuously for each student over a period of time, dropout prediction is essentially a time series prediction problem. By regarding dropout prediction as a sequence classification problem, we propose some temporal models for solving it. In particular, based on extensive experiments conducted on two MOOCs offered on Coursera and edX, a recurrent neural network (RNN) model with long short-term memory (LSTM) cells beats the baseline methods as well as our other proposed methods by a large margin.