Risk & Interventions
Identify at-risk students early with predictive scores and create targeted success plans that drive completion.
Michael Smith
Composite Risk Score
78/100
Proactive Intervention Tools
Move from reactive support to preventative action with AI-driven insights.
Predictive Risk Scores
Machine learning models that identify students at risk of not persisting, updated in real-time based on engagement and academic data.
Intervention Outcomes
Don't let students slip through the cracks. Intervene at the precise moment support is needed with data-driven precision.
Identify at-risk students 3-4 weeks earlier with real-time behavioral data.
Prioritize caseloads by severity to help the most at-risk students first.
Improve efficacy by matching specific risk factors to the right support.
Close the feedback loop by linking interventions to retention outcomes.
Reduce advisor 'alert fatigue' by filtering false positives with AI.
Scale student care capacity by automating routine check-ins and referrals.