Student Dropout Predictor (Early Warning System)
Model Description
This model is a Random Forest classifier designed to predict the likelihood of student dropout (academic churn) in a virtual learning environment. It serves as the core analytical engine for an Early Warning System (EWS) that triggers proactive generative AI interventions.
- Model Type: Random Forest Classifier
- Task: Tabular Classification (Binary: Dropout vs. Retained)
- Language: English/Spanish feature support
- Interpretability: Fully compatible with SHAP (SHapley Additive exPlanations) to extract individual risk factors.
Intended Use
This model is intended for educational institutions to identify at-risk students based on their telemetry and engagement data (clicks, VLE interactions, assignment submissions, and demographics).
It is designed to be used in conjunction with a generative AI agent (e.g., Claude 3.5 via AWS Bedrock) to draft personalized intervention emails based on the root causes of the predicted risk.
Training Data
The model was trained on an anonymized dataset comprising student demographics, historical academic performance, and daily interaction logs with the Virtual Learning Environment (VLE).
Features
The input features include, but are not limited to:
vle_clicks: Total interactions with the digital platform.attendance_days: Active days in the system.assessments_submitted: Number of assignments handed in.- Demographics: Age band, disability status, prior education level.
Metrics
(Note: Update these metrics based on your final evaluation script)
- Accuracy: 0.XX
- F1-Score: 0.XX
- ROC-AUC: 0.XX
