Comprehensive tools for early intervention and academic success
Advanced machine learning models analyze academic patterns to predict student risk with 89% accuracy.
Comprehensive tracking of academic progress across semesters with predictive trends.
Automated, detailed reports for students, faculty, and parents with actionable insights.
Personalized recommendations for at-risk students based on their specific challenges.
Centralized dashboard for faculty to monitor all students and track intervention effectiveness.
Hardware integration for real-time student presence and engagement tracking.
Our ensemble machine learning model combines multiple algorithms to achieve superior accuracy in identifying at-risk students.
| Feature | Traditional Systems | EduRisk |
|---|---|---|
| Detection Timeline | After failure (reactive) | 8-12 weeks early (predictive) |
| Data Sources | Grades only | Grades + Attendance + IoT + Engagement |
| Automation | Manual monitoring | Fully automated ML pipeline |
| Recommendations | Generic advice | Personalized AI-driven |
| Real-time Updates | Weekly/Monthly | Real-time dashboard |
Explore our workflow or contact us for a live demonstration.