Massive Open Online Courses Temporal Profiling for Dropout Prediction

TL;DR

This study analyzes learner dropout behavior in MOOCs by extracting behavioral features and comparing classifiers to predict dropout timing, revealing that active engagement indicators are key predictors of course persistence.

Contribution

It introduces a detailed feature extraction approach for MOOC dropout prediction and compares multiple classifiers for two different prediction tasks.

Findings

Active user behaviors strongly predict persistence.

Prediction accuracy is higher for overall dropout vs. weekly dropout.

Logistic Regression performs slightly better on weekly dropout prediction.

Abstract

Massive Open Online Courses (MOOCs) are attracting the attention of people all over the world. Regardless the platform, numbers of registrants for online courses are impressive but in the same time, completion rates are disappointing. Understanding the mechanisms of dropping out based on the learner profile arises as a crucial task in MOOCs, since it will allow intervening at the right moment in order to assist the learner in completing the course. In this paper, the dropout behaviour of learners in a MOOC is thoroughly studied by first extracting features that describe the behavior of learners within the course and then by comparing three classifiers (Logistic Regression, Random Forest and AdaBoost) in two tasks: predicting which users will have dropped out by a certain week and predicting which users will drop out on a specific week. The former has showed to be considerably easier, with all three classifiers performing equally well. However, the accuracy for the second task is lower, and Logistic Regression tends to perform slightly better than the other two algorithms. We found that features that reflect an active attitude of the user towards the MOOC, such as submitting their assignment, posting on the Forum and filling their Profile, are strong indicators of persistence.