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The traditional ITS have considered the learners as a knowledge receiver. The recent development of teachable agent make it possible to provide the learner with an active role as a knowledge constructor and to take initiatives to persist in learning. In order to make an adaptive teachable agent that responds intelligently for individual learner, it should reflect the individual differences in the level of cognition and motivation, and its ongoing changes. For the purpose of developing individualized teachable agent, it is proposed to a student model based on the correlation among three dimensions: individual differences, learner responses, and learning outcome. A correlation analysis among the log data, questionnaire scores, and learning measurements was conducted. We delineated the relationships among three dimensions, learner responses (mouse-click pattern, duration & frequency at particular task, individual choice etc), individual characteristics (metacognitive awareness, self-efficacy, learning goal, and performance goal), and learning outcomes (interest and comprehension) during interacting with the teachable agent. The results suggest that certain type of learner responses or the combination of the responses would be useful indices to predict the learners' individual characteristics and ongoing learning outcome. [ABSTRACT FROM AUTHOR]

Most of the intelligent tutoring system (ITS) is used to deliver knowledge and train skills based on the expert model. According to the theories of leaning, learning by teaching method is more efficient for enhancing motivation to learn and cognitive ability than learning by listening or learning by reading. For the purpose of developing an adaptive intelligent agent to enhance the motivation to learn, the new type of teachable agent were designed and implemented, the KORI (KORea university Intelligent agent), in which the user plays a role of a tutor by teaching the agent using a concept map, posing questions, and providing feedbacks. KORI consists of four independent modules: teach module, Q&A module, test module, and resource module. In teach module, the KORI's knowledge is structured and organized through the concept map and the KORI makes new knowledge from the inference engine. In Q&A module, the KORI can provide answers to the users' questions through an interactive window. It is expected that providing the user with the active role of teaching the agent enhance the motivation to learn and the positive attitude toward the subject matter as well as cognition. Keywords: TA (Teachable Agent), intelligent tutoring system, learning by teaching, concept map, motivation to learn, inference engine, knowledge representation. [ABSTRACT FROM AUTHOR]