Using Feature Selection and Unsupervised Clustering to Identify Affective Expressions in Educational Games

Educational games can induce a wide range of emotions, and so recognizing specific emotions may be valuable for an intelligent system that aims to adapt to varying student needs so as to improve learning. The long-term goal of this work is to understand how user affect impacts overall learning in an educational game. The main contribution of this paper is an investigation into the use of an unsupervised machine learning technique to help recognize meaningful patterns in biometric affective data. Results show that this method can identify interesting and sensible student reactions to different game events.