---
language:
- en
- es
tags:
- education
- early-warning-system
- sklearn
- tabular-classification
- random-forest
---

# Student Dropout Predictor (Early Warning System)

## Model Description
This model is a Gradient Boosting Classifier 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:** 0.77
- **F1-Score:** 0.78
- **ROC-AUC:** 0.84