ENGAGE: Early Notification & Guidance for Academic Growth & Engagement

Digital twin technology for comprehensive student engagement analysis enabling proactive interventions for at-risk students.

The ENGAGE testbed is focused on determining if a digital twin can be used to identify and support at-risk students. The testbed will create a comprehensive digital twin system that provides an at-risk holistic view that integrates academic scores, class participation, extracurricular involvement, behavioral indicators, and sentiment analysis to identify emotional and engagement signals that are critical for student retention.

The digital twin technology synthesizes diverse data streams from school information systems, learning management platforms, and extracurricular tracking tools to simulate student engagement trajectories. Through advanced analytics and sentiment analysis, the system generates monthly diagnostic reports that provide educators and student affairs professionals with actionable insights about student climate, belonging, and engagement patterns that have never been measurable before.

Expected outcomes include demonstrating significant improvements in intervention timing and effectiveness, with the digital twin providing lead time advantages over traditional methods. The testbed will establish baseline measurements for previously unmeasured sentiment indicators and validate the correlation between digital twin-identified leading indicators and critical lagging indicators such as retention and academic persistence.

The ENGAGE testbed contributes to industry advancement in the following ways:

1. Educational Standards Development: Establishing standards for digital twin applications in education and student well-being monitoring, creating frameworks for ethical and effective implementation across higher education institutions.
2. Data Integration Best Practices: Providing validated methodologies for integrating academic, behavioral, and engagement data while maintaining privacy compliance with FERPA regulations in community college environments.
3. Early Warning Methodologies: Advancing knowledge on scalable early warning systems for K-12 and higher education, demonstrating how digital twins can identify at-risk students through comprehensive engagement analysis.
4. Privacy-Preserving Analytics: Demonstrating ethical, privacy-preserving analytics approaches in educational settings, establishing guidelines for sensitive student data handling and sentiment analysis.
5. Intervention Framework Development: Creating validated frameworks for translating digital twin insights into actionable intervention strategies, bridging the gap between data analytics and student support services.
6. Societal Benefit Applications: Proving digital twin technology's capability for societal benefit applications, showcasing how advanced analytics can support student well-being and educational success at scale.

Lead Developer