Demonstrating REACT: a Real-time Educational AI-powered Classroom Tool
This is an incremental tool for educators to improve course planning by leveraging educational data mining techniques.
The authors tackled the problem of supporting educators' decision-making by developing REACT, a real-time AI tool that analyzes student performance data to provide alerts and recommendations, demonstrated through a use case where it clustered students based on incorrect responses and hints used to help identify at-risk students and form study groups.
We present a demonstration of REACT, a new Real-time Educational AI-powered Classroom Tool that employs EDM techniques for supporting the decision-making process of educators. REACT is a data-driven tool with a user-friendly graphical interface. It analyzes students' performance data and provides context-based alerts as well as recommendations to educators for course planning. Furthermore, it incorporates model-agnostic explanations for bringing explainability and interpretability in the process of decision making. This paper demonstrates a use case scenario of our proposed tool using a real-world dataset and presents the design of its architecture and user interface. This demonstration focuses on the agglomerative clustering of students based on their performance (i.e., incorrect responses and hints used) during an in-class activity. This formation of clusters of students with similar strengths and weaknesses may help educators to improve their course planning by identifying at-risk students, forming study groups, or encouraging tutoring between students of different strengths.