Predicting Co-occurring Emotions in MetaTutor when Combining Eye-Tracking and Interaction Data from Separate User Studies

Abstract

Learning can be improved by providing personalized feedback adapting to the emotions that the learner may be experiencing. There is initial evidence that co-occurring emotions can be predicted during learning in Intelligent Tutoring Systems (ITS) through eye-tracking and interaction data. Predicting co-occurring emotions is a complex task and merging datasets has the potential to improve predictive performance. In this paper, we combine data from two user studies with an ITS, and analyze whether there is an improvement in predictive performance of co-occurring emotions, despite the user studies using different eye-trackers. In the pursuit towards developing real affect-aware ITS, we look at whether we can isolate classifiers that perform better than a baseline. In this regard we perform a series of statistical analyses and test out the predictive performance of standard machine learning models as well as an ensemble classifier for the task of predicting co-occurring emotions.