Building a Corpus and a Local Binary Pattern Recognizer for Learning-Centered Emotions.

Studies investigating the effectiveness of affect detection inside intelligent learning environments (ILEs) have reported the effectiveness of including emotion identification on learning. However, there is limited research on detecting and using learning-centered data to investigate metacognitive and affective monitoring with ILEs. In this work we report the methodology we follow to create a new facial expression corpus from electroencephalography information, an implementation of an algorithm and a training of an SVM to recognize learning-centered emotions (frustration, boredom, engagement and excitement). Also, we explain changes realized in a fuzzy logic system into an intelligent learning environment. The affect recognizer was tested into an ILE for learning Java programming. We present successful results of the recognizer using our corpus face database and an example test using our ILE.