An AI-Powered Learning Analytics Dashboard for Real-Time Student Performance and Motivation Monitoring

Le résumé fourni par la source

This work aims to present an AI-powered Learning Analytics Dashboard (LAD) designed to predict student performance and motivation in real-time, utilizing behavioral data from Learning Management Systems (LMS). The system utilizes machine learning models to predict academic outcomes and engagement levels, allowing educators to identify at-risk students and implement targeted interventions promptly. The dashboard was validated using data from three undergraduate and graduate engineering courses, achieving high predictive accuracy, with Mean Hassanat Similarity Percentage (MHSP) scores exceeding 95 % in large cohorts. Results demonstrate the system's adaptability across diverse course contexts and highlight its effectiveness in supporting data-driven, personalized teaching strategies. By integrating predictive analytics and actionable insights into a user-friendly interface, this work advances the use of AI in education and contributes to more responsive, student-centered learning environments.

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