Classifying interaction behaviors of students and conversational agents through dialog analysis

Abstract

E-learning systems based on a conversational agent (CA) provide the basis of an intuitive and engaging interface for the student. The goal of this paper is to propose a method for detecting conversational interaction behaviors of learners and CAs, using an agent-based framework, for the purpose of improving the communication between students and CA-based intelligent tutoring systems. Our framework models both the student and the CA and uses agents to represent data sources for each. We show how the framework uses the detection of conversational behaviors to initiate interventions to improve student conversational engagement. The results of initial user testing are reported.