Predicting who might drop out is easy. Acting on it fairly, transparently and in time is the hard part.
Explainable beats accurate
Advisers will only act on a flag they understand. Show the three strongest signals behind each risk score, and let staff record why they did or did not intervene.
Guard against bias
Test performance across demographic groups before launch and monitor it each term. Never use protected attributes as inputs, and check proxies such as postcode.
Design the intervention first
A risk list without a support process changes nothing. Agree who contacts the student, within what time, and with what offer before the model goes live.
Working through this in your organisation?
Talk to the team behind our Education practice about what a practical first step looks like for you.