Revolutionizing student success through artificial intelligence and IoT
Every year, thousands of students struggle academically without early intervention. Traditional monitoring systems identify issues too late.
Reactive approaches lead to dropout rates of 15-20% in engineering programs.
Time that could be spent on meaningful interventions.
Attendance, grades, and engagement exist in separate systems.
can be predicted by the end of the first semester with ML models
Source: Educational Data Mining Research 2024
Machine learning models analyze academic patterns to flag at-risk students 8-12 weeks before traditional methods.
RFID attendance, library access, and lab usage provide real-time engagement data to the ML pipeline.
Personalized recommendations for students and alerts for faculty enable timely interventions.
Reduction in academic probation cases
Faster intervention time
Prediction accuracy
Real-time monitoring