Predicting Student Performance
Ai/ml Case Study — Predicting Student Performance & Preventing Dropouts
The Background
A reputable coaching institute with 600+ students across multiple centers noticed a worrying pattern: once engaged students suddenly dropping performance and disengaging, without teachers realizing early enough.
Parents complained that they learned about problems “too late to fix.”
The institute needed a proactive solution that identified struggling students before they reached a breaking point.
The Problem
Early Warning Signs Were Missed
Attendance & Assignments Were Tracked Manually
Parent Communication Was Reactive
No Insight Into Behavioral Data
The Trigger
A bright student unexpectedly dropped out 3 months before exams.
Leadership realized the institute was reactive, not proactive.
This started their journey toward AI-driven student intelligence.
Cor Advance Data Solution
We created a Student Risk Prediction Engine analyzing
Attendance
- Machine IoT logs
- ERP order data
- QC inspection results
- Maintenance schedules
- Operator productivity
Timely assignment submission
- Production efficiency (by line, shift, operator)
- QC pass/fail trends
- Downtime root cause drill-down
- Procurement & supplier scorecards
- Raw material turnover
- Rework & scrap analysis
Test performance trends
We fed predictions back into dashboards to show:
- At-risk machines
- Expected delays
Predicted production output
Class participation
- Machine IoT logs
- ERP order data
- QC inspection results
- Maintenance schedules
- Operator productivity
Question attempt patterns
- Production efficiency (by line, shift, operator)
- QC pass/fail trends
- Downtime root cause drill-down
- Procurement & supplier scorecards
- Raw material turnover
- Rework & scrap analysis
Engagement with digital content
We fed predictions back into dashboards to show:
- At-risk machines
- Expected delays
Predicted production output
Implementation Journey
Weeks 1-2
Discovery & Requirement Analysis
Dropout rate dropped by 19%
Week 4
Blockchain Strategy & Planning
Average test scores improved by 12%
Week 6
Smart Contract & DApp Development
Teachers saved hours of manual tracking
Week 8
Smart Contract & DApp Development
Parents appreciated proactive updates
Transformation & Results
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