Predicting Quitting Behavior in SQL-Tutor

Abstract

Although Intelligent Tutoring Systems (ITSs) have proven to be very effective in supporting learning, keeping students who interact with them engaged in their activity remains a challenge. In this study, we use machine learning techniques to predict whether the student is going to abandon the current problem. The study has been done in the context of SQL-Tutor, a constraint-based ITS that teaches students how to query relational databases. We extracted a number of features from past data and used the J48 algorithm to train a decision tree. The model was used in a lab session to make predictions and provide limited intervention in order to prevent potential abandonments. Overall, the classifier demonstrated a promising performance. The results also provided insights as to what areas can be improved in future.