Investigation of a Method to Estimate Learners' Interest Level for Agent-Based Conversational e-Learning

A method for recognizing or estimating learners’ affective state plays a key role for realizing agent-based conversational e-Learning. In this paper, we focus on the learners’ interest level as an example of the important affective state, and investigate a method for estimating it from their nonverbal behaviors. In conversational situations, the sense of the nonverbal behaviors will vary depending on the contexts of the conversations. Therefore we do not use the nonverbal behaviors themselves but use the occurrence frequencies of the nonverbal behaviors as inputs for estimation mechanism. In the result of our experiment, the proposed method could estimate whether the learners’ interest level is “High” or “Low” with the accuracy of more than 70%.