Bayesian Agent in e-Learning

This paper proposes an agent that acquires the domain knowledge concerned with the content from a learning history log database and automatically generates motivational messages. The unique features of this system are as follows: The agent builds a learner model automatically by applying the Bayesian network. The agent predicts a learner´s final status (1.Failed, 2. Abandon, 3. Successful, 4.Excellent) using the learner model and his/her current learning history log data. 3. The agent compares a learner´s learning processes with excellent learners´ learning processes in the database, diagnoses the learner´s learning processes and generates adaptive messages to the learner. The comparisons between the proposed method and the agent using the decision tree show that the proposed method has better prediction performances and effective to degrease the number of students withdrew from classes.