AI
Student Success Centre
Predictive analytics for Acacia College — identify at-risk students before failure, dropout, or fee default.
High failure risk
1
Dropout risk
1
Fee default risk
1
Risk students
| Student | Programme | Failure risk | Dropout risk | Fee default | Graduation likelihood |
|---|---|---|---|---|---|
John Mbeki AC2021045 | DIP-ED | 92% | 45% | 20% | 28% |
Paul Tjiueza AC2021032 | DIP-ED | 74% | 38% | 74% | 55% |
Mary Kapena AC2021088 | BED-FP | 68% | 80% | 35% | 42% |
Demo Student SC2026001 | BED-FP | 18% | 12% | 25% | 86% |
Models combine attendance, marks, fee history, and engagement signals. Production deployments can use OpenAI or on-premise models with institution-specific training data.