Analyze a Learning-Analytics Dataset for At-Risk Detection
Overview
What this challenge is about.
Build a classifier from LMS engagement data to predict at-risk students by week 4, then audit fairness. Earn a verifiable certificate.
The scenario
The university (around 24,000 students, around 8 schools) has had 2 prior false-start at-risk-modeling projects that were either too late (predictions only available at week 10) or too biased (false-positive rate doubled for first-generation students).
The Brief
What you'll do, and what you'll demonstrate.
Build a week-4 at-risk classifier that's accurate AND fair across protected groups, with an operationalization memo for the dean.
Earning criteria — what you'll demonstrate
- Engineer time-window features for learning-analytics tasks
- Calibrate a classifier and report precision-recall meaningfully
- Audit a learning-analytics model across protected attributes
- Communicate at-risk modeling to a dean's office audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Learning Analytics
Apply learning analytics to solve real industry problems and demonstrate production-level capability.
- Classification
Apply classification to solve real industry problems and demonstrate production-level capability.
- Fairness Metrics
Apply fairness metrics to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Scikit Learn
Apply scikit learn to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Data Scientist
Learning-analytics modeling with a fairness audit is the canonical data-scientist project at universities and edtech companies.
This challenge sharpens
- learning-analytics
- classification
- fairness-metrics
Machine Learning Engineer
Calibrated classifiers with no-leakage time-window features and operationalization plans are the MLE shape at edtech shipping ML.
This challenge sharpens
- classification
- feature-engineering
- scikit-learn
AI Safety Researcher
Fairness auditing on consequential decision systems is the responsible-AI lens that safety researchers bring to vertical AI work.
This challenge sharpens
- fairness-metrics
- model-evaluation
- learning-analytics