(untitled) (International Conference on Computers in Education, 2020)
Most of the proposed intelligent tutoring systems (ITSs) intend for learners to cultivate a better understanding of what is written explicitly in texts regarding a target learning domain. However, these ITSs do not consider the improvement of learners’ awareness of their own understanding state. To solve this problem, we develop a method for building a learner model that captures learners’ misguided self-confidence in their own comprehension. More specifically, we set up a situation that allowed learners to proceed with presentation design activity using semantics-aware presentation materials (slides and terms of learning goals). In this situation, the materials have asymmetric structures between information that learners can recognize explicitly and computers can process. Based on these characteristics, our learner model allows ITS to capture what learners should set as learning goals, even if the contents are not described explicitly in the texts.