Student Retention & At-Risk Early Warning

Students who disengage and fail to complete their qualification reduce both your outcomes data and your government funding entitlements. There is currently no systematic way to identify at-risk students early enough for meaningful intervention - by the time a student drops out, the funding is already lost.

A predictive model trained on historical student data - enrolment patterns, attendance signals, assessment submission rates, and demographic indicators - that scores each active student weekly on their completion risk, and triggers automated alerts and intervention prompts to trainers and student support staff.