Ranking Feature Sets for Emotion Models Used in Classroom Based Intelligent Tutoring Systems

Abstract. Recent progress has been made by using sensors with Intelligent Tutoring Systems in classrooms in order to predict the affective state of students users. If tutors are able to interpret sensor data with new students based on past experience, rather than having to be individually trained, then this will enable tutor developers to evaluate various methods of adapting to each student’s affective state using consistent predictions. In the past, our classifiers have predicted student emotions with an accuracy between 78% and 87%. However, it is still unclear which sensors are best, and the educational technology community needs to know this to develop better than baseline classifiers, e.g. ones that use only frequency of emotional occurrence to predict affective state. This paper suggests a method to clarify classifier ranking for the purpose of affective models. The method begins with a careful collection of a training and testing set, each from a separate population, an...

• Type: Conference
• Year: 2010
• Where: UM
• Authors: David G. Cooper, Kasia Muldner, Ivon Arroyo, Beverly Park Woolf, Winslow Burleson
• Keywords: Affective States, Classifier Rankings, Programming Languages, Student’s Affective State, UM 2010