XGBoost + SHAP model for student engagement detection in low-resource African learning environments using the OULAD dataset
# AfriLearn Lens
Behavioural engagement detection for students in low-resource
African learning environments using the OULAD dataset.
Link : AfriLearn Lens: Explainable…
## Overview
AfriLearn Lens applies XGBoost and SHAP explainability to predict
student engagement levels (Low, Medium, High) from passive
Virtual Learning Environment (VLE) behavioural signals — without
relying on demographic features.
## Key Finding
SHAP analysis shows that behavioural features (active_days,
unique_resources) are far stronger predictors of engagement than
demographic features (gender, region, deprivation band) —
challenging assumptions common in African education research.
## Model Performance
- Accuracy: 80.78%
- Best class: Low engagement (precision 0.92, recall 0.90)
- Model: XGBoost classifier (200 estimators, max depth 6)
## Dataset
OULAD — Open University Learning Analytics Dataset
Source: Kaggle (anlgrbz/student-demographics-online-education-dataoulad)
## Features Used
- active_days — number of unique days active on VLE
- unique_resources — number of distinct resources accessed
- resource_rate — exploration intensity (resources per active day)
- consistency — regularity of access (active days per resource)
- Demographics: gender, region, highest_education, imd_band,
age_band, disability, num_of_prev_attempts, studied_credits
## Results
### Global Feature Importance (SHAP)
### Confusion Matrix
## Repository Structure
- AfriLens_Code.ipynb — full pipeline notebook
- shap_global_importance.png — SHAP bar chart
- shap_class_high.png — SHAP beeswarm for High class
- shap_class_low.png — SHAP beeswarm for Low class
- shap_class_medium.png — SHAP beeswarm for Medium class
- confusion_matrix.png — confusion matrix heatmap
## Author
Abdussomad Olayiwola
400-level B.Tech Computer Science, LAUTECH
AI and Data Science Instructor, Africa Research Center (ARC)
GitHub: github.com
LinkedIn: linkedin.com