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Education / AI / Learning Analytics

DigitalTwin

Student Digital Twin analyzes academic engagement, interaction, and performance data to predict and support student success.

Research year

2025

Student

InfoDevs

DigitalTwin poster

Research summary

Project overview

The Student Digital Twin is a learning analytics platform that creates a real-time virtual model of a student’s academic journey. It integrates multiple data sources such as learning management systems, campus access data, academic records, and classroom interactions to monitor student engagement and performance. The system provides four main dashboards—Profile, Engagement, Interactivity, and Academics—to give educators and administrators a comprehensive view of student activity and progress. Using analytics and predictive insights, the platform can identify trends such as thriving performance, balanced progress, or potential burnout risks. By combining real-time data with predictive analytics, the platform helps universities detect potential academic issues early, support student wellbeing, and improve overall student success outcomes.