< Modeling Study for Developing Motivational and Cognitive Adaptive Agent >

Recent development of teachable agent provides learners with active roles as knowledge constructors and focuses on the individualization. The aim of this adaptive agent is not only to maximize the learner's cognitive functions but also to enhance the interests and motivation to learn. In order to establish the relationships among user characteristics and response patterns and to extract the algorithm among variables, we measured the individual characteristics and analyzed logs of the teachable agent named KORI (KORea university Intelligent agent) through the student modeling. A correlation analysis was conducted to identify the relationships among individual characteristics, user responses, and learning outcomes. Among hundreds of possible relationships between numerous variables in three dimensions, nine key user responses were extracted, which were highly correlated with either individual characteristics and learning outcomes. 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. This study proposed a new type of dynamic assessment for individual differences and ongoing cognitive/motivational learning outcomes through the computation of responses without measuring them directly. The construction of individualized student model based on the ongoing response pattern of the user that are highly correlated with the individual differences and learning outcome may be the useful methodology to understand the learner's dynamic change during learning.