Animated pedagogical agent based on decision tree for e-learning

This paper proposes a LMS (learning management system) with intelligent agent to provide effective adaptive messages to a learner. The unique features of this paper are shown as follows: The agent system proposed in this paper has a learner model, which is automatically and continually constructed by applying the decision tree model constructed from the learning histories data stored in the data-base. The constructed leaner model predicts a learner´s future final status (1. Failed, 2. Abandon, 3. Successful, 4.Excellent) using his/her current learning history data. The constructed leaner model becomes more exact as the amount of data accumulated in the database increases. The agent system presents the optimal instructional message based on the learner´s predicted future state. The agent provides some attention cues according to Ueno (2004) at the timing when a learner begins to be bored with his/her learning. In addition, this paper demonstrates the effectiveness of this system through actual e-learning classes.