Student Risk Dashboard
An XGBoost classifier that flags at-risk students early, wired into a live dashboard built for faculty with no ML background.
Faculty Dashboard
What faculty actually see — plain-language risk tiers, not raw model output.
Case File
Three angles on the same build — click through.
Faculty had academic and attendance data spread across spreadsheets, updated inconsistently across departments, with no early-warning signal for students likely to underperform or drop out.
By the time a student's struggle showed up in a mid-term or final grade, it was often too late to intervene meaningfully. The gap wasn't a lack of data — it was that nobody could turn scattered records into a signal fast enough to act on.
- Data Cleaning & Merging — pulled multi-semester academic, attendance, and engagement records from separate spreadsheets into one consistent structure faculty had never had before.
- Feature Engineering — built features that captured trend over time (is attendance dropping, not just what it is today), not just a single snapshot score.
- Model Training — trained and tuned an XGBoost classifier, benchmarked against logistic regression and random forest baselines to confirm it was actually the better fit, not just the fashionable one.
- Dashboard Layer — built a dashboard that translates raw model output into plain-language risk tiers (Low / Medium / High), so a non-technical faculty member never has to interpret a probability score.
- Validation — checked predictions against historical outcomes to catch false positives before faculty started acting on them.
A working risk-scoring pipeline faculty could act on directly, without needing to interpret raw model scores themselves. Attendance, assignment, and engagement data that used to sit in disconnected spreadsheets now feeds a single risk view, updated as new data comes in.
The bigger lesson: a model is only useful if the people meant to act on it can actually read it. Most of the real design work here wasn't the XGBoost tuning — it was translating a probability into something a professor with zero ML background could glance at and understand in seconds.
Pipeline
From scattered spreadsheets to a single risk score, in four stages.
Try the Risk Model
Adjust the sliders to see how attendance and performance signals shift a student's risk tier.
What the Model Catches
Attendance 52%, Sharply Declining
Assignment average dropped from 71% to 38% over the semester — the trend, not the snapshot, is what triggers the flag.
Attendance 93%, Stable
Consistent scores across assignments and mid-terms, no negative trend in engagement or attendance.
Stack
Click any tag to see how it's actually used in this project.
Click any technology above to see exactly how it's used in this build.