Learning with Explainable AI-Recommendations at School: Extracting Patterns of Self-Directed Learning from Learning Logs

• Main Authors: Majumdar, Rwitajit, Takami, Kyosuke, Ogata, Hiroaki
• Format: Conference Proceeding
• Language: English
• Subjects: Artificial intelligence; Bayes methods; Education; Electronic publishing; LEAF; pattern mining; SDL; XAI
• ISSN: 2161-377X
• DOI: 10.1109/ICALT58122.2023.00078

Educational explainable AI (XAI) applications are gaining research focus and have distinct needs in the domain of Education. This research presents Educational eXplainable AI Tool (EXAIT), a system for math quiz recommendations, along with an explanation. EXAIT was implemented in a Japanese public high school where students received the top 5 math problems based on Bayesian Knowledge Tracing (BKT) algorithm in a learning analytics dashboard. It aimed to help them complete their summer vacation assignments having 240 questions. On click, the students were redirected to an eBook platform to submit their accuracy and confidence level in each problem. We conducted a study with a quasi-experimental design and divided into 3 groups based on compliance of use. RecoExp group received and used explanations regarding why an item was recommended and how it aims to maximize learners' knowledge-gaining path. RecoCon was the control group that received just the recommendations and used it and RecoNone group did not use the system at all during the time period. We provide a framework to analyze learning logs from EXAIT and extract emerging self-directed learning patterns. Analyzing 222 students' EXAIT logs, we found learners who had checked explanations while selecting recommendations had significantly higher performance. Further differential process mining highlighted significant active daily engagement transitions of the RecoExp group in the self-directed activity.