Student Analytics

Machine learning models that predict student retention risk and identify the highest-impact intervention opportunities.

Michael Smith

Student • Computer Science

Accounting

Opportunities

Moderate

Persistence

GPA: indicates moderate performance.

Engagement: Last logged in 5 days ago.

Honors: Participation shows dedication.

Actuarial Science

Opportunities

High

Persistence

GPA: indicates strong performance.

Engagement: Daily active login streak.

Online: Requires self-discipline.

Identify risks early and measure the impact of every intervention.

Machine-learning models identify which students are most at risk of not persisting, enabling early outreach.

Don't let students slip through the cracks. Intervene at the precise moment support is needed with data-driven precision.

Identify at-risk students 4-6 weeks earlier via engagement signals.

Predict persistence risk with >85% accuracy using historical data.

Reduce dropouts by surfacing specific drivers for every flagged student.

Optimize outreach by prioritizing the 10-15% most likely to benefit.

Measure program efficacy by tracking post-intervention retention.

Eliminate bias by focusing on behavioral patterns, not demographics.