Risk Analysis

Identify students at risk of missing graduation earlier in the education process using predictive analytics and district‑specific models—so teams can target interventions sooner and more effectively.

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Algorithms based on your district’s historical data + critical factors.

Explainability: see which factors contribute most to risk.

High school students who graduate on time are more likely to continue to postsecondary education and training (KIDS COUNT Child Well‑Being Index). [web:315]

What you get• Predictive insights

Better predict outcomes with an algorithm based on your district’s historical data and other critical factors. [web:315][web:316]

Increase intervention effectiveness with data-driven insights into why a student is at risk of not graduating on time. [web:315][web:316]

Quickly identify risk levels to improve the accuracy of your risk assessments. [web:315][web:316]

District-specific predictive models can segment by grade and time of year, and categorize students into risk levels. [web:316]

A better way to identify students at‑risk

Risk Analysis combines district-specific predictive models, multiple risk views, and transparency into contributing factors so teams can act with confidence. [web:316]

District-specific predictive models

Use your district data to develop predictions and segment by grades and time of year.

Multiple risk analysis tools

Support different needs with multiple graduation risk views and distinct tools.

Transparency to factors

See which student data factors increased risk to guide targeted intervention.

Risk level grouping

Group students into high/medium/low risk categories to allocate resources better.

Predictive analytics with explainable drivers

Machine learning connects factors like attendance, behavior, credits, GPA, and assessments to graduation probability—so the “why” is visible, not a black box. [web:316]

Overall probability

Predict graduation likelihood using ML models tuned to your district.

Factor-level insights

Highlight which factors contribute most to risk for each student.

District-controlled thresholds

Keep ML thresholds or override them with district-defined thresholds.

Risk driver snapshotExplainable

Attendance

Trend down → increases risk.

Credits

Off‑track → missing requirements.

GPA

Drop detected → monitor closely.

Behavior

Incidents cluster → support needed.

Act earlier

Identify risk → understand drivers → intervene → improve graduation outcomes.

Ready to identify risk earlier?

Use district-specific predictive models and explainable risk drivers to focus interventions where they’ll make the biggest difference. [web:315][web:316]

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