Predicting student disengagement: Harnessing visual cues for intelligent tutoring systems

Abstract

Intelligent tutoring systems have the potential to enhance the learning experience for children, but it is crucial to detect and address early signs of disengagement to ensure effective learning. In this paper, we propose a method that utilizes visual features from a tablet tutor's user-facing camera to predict whether a student will complete the current activity or disengage from it. Unlike previous approaches that relied on tutor-specific features, our method leverages visual cues, making it applicable to various tutoring systems. We employ a deep learning approach based on a Long Short Term Memory (LSTM) model with a target replication loss function for prediction. Our model is trained and tested on screen capture videos of children using a tablet tutor for learning basic Swahili literacy and numeracy in Tanzania. With 40% of the activity remaining, our model achieves a balanced-class size prediction accuracy of 73.3%. Furthermore, we analyze the variation in prediction accuracy across different tutor activities, revealing two distinct causes of disengagement. The findings indicate that our model can not only predict disengagement but also identify visual indicators of negative affective states that may not lead to non-completion of the task. This work contributes to the automated detection of early signs of disengagement, which can aid in improving tutoring systems and guiding pedagogical decisions in real-time.