京都大学大学院(博士課程) / 情報学研究科
Identifying Students' Stuck Points Using Self-Explanations and Pen Stroke Data in a Mathematics Quiz
Ryosuke Nakamoto, Brendan Flanagan, Kyosuke Takami, Dai, Hiroaki Ogata 2021年11月 During the process of learning, students face challenging quizzes that require various knowledge and skills, which results in stuck points. Identifying concepts that cause these stuck points can help identify potential areas of remedial study to overcome the students' difficulties. To achieve the goal of generating a model to identify students' stuck points in a mathematics quiz, we attempted to discover highly influential features using self-explanations and pen stroke data collected from