Dropout Prediction

It identifies at-risk students before they disengage by synthesizing academic, financial, and behavioral signals to power proactive institutional retention.

It runs multi-dimensional analysis across four areas: Attendance Drop (subtle patterns of missing early morning classes or specific labs), Academic Velocity (relative rank in the cohort dropping consistently over three weeks), Financial Signals (delayed tuition payments detected via the ERP), and LMS Disengagement (login frequency and assignment view-times relative to the class average).

The retention pipeline has four stages: ingest system data, compute risk probability, trigger an advisor alert, and execute mitigation.

The engine builds an automated action plan for that learner profile. For a student flagged with high dropout probability it can email the faculty advisor, flag the student for a peer mentoring group, and auto-schedule a counseling session pending confirmation.

By the time a student misses an exam or fails a semester, the decision to drop out has usually already been made. Catching the micro-signals of disengagement weeks before they compound buys support staff the window they need to intervene and retain the student.