Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction
Keywords:Summarization, Group Activity, Anomaly Detection, Performance Prediction
AbstractLearning exercises that activate students’ additional cognitive understanding of course concepts facilitate contextualizing the content knowledge and developing higher-order thinking and problem-solving skills. Student-generated instructional materials such as course summaries and problem sets are amongst the instructional strategies that reflect active learning and constructivist philosophy. The contributions of this work are twofold: 1) We introduce a practical implementation of inside-outside learning strategy in an undergraduate deep learning course and will share our experiences in incorporating student-generated instructional materials learning strategy in course design, and 2) We develop a context-aware deep learning framework to draw insights from the student-generated materials for (i) Detecting anomalies in group activities and (ii) Predicting the median quiz performance of students in each group. This work opens up an avenue for effectively implementing a constructivism learning strategy in large-scale and online courses to build a sense of community between learners while providing an automated tool for instructors to identify at-risk groups.
How to Cite
Norouzi, N., & Mazaheri, A. (2023). Context-Aware Analysis of Group Submissions for Group Anomaly Detection and Performance Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15938-15946. https://doi.org/10.1609/aaai.v37i13.26892
EAAI Symposium: AI for Education