Akaike’s Early Warning System Reduced Student Dropout Rate by 12%
A school district serving over 4,000 students in low-income and underserved communities faced a pressing challenge: rising dropout rates among middle schoolers.
Many students were disengaging from the learning process as early as grades 6–8. By the time warning signs became obvious, it was often too late for intervention. Teachers and staff struggled to identify at-risk students early enough to provide meaningful preventative measures.
Challenges
Disparate data sources scattered across multiple platforms.
Time-consuming manual data checks that delayed interventions.
Lack of a unified metric to quantify dropout risk in real time.
Our Solution
Unified data infrastructure.
Real-time student watchlist & alert system.
Predictive analytics to forecast student’s risk score based on historical data.
Results
We created a dropout risk dashboard based on three key data buckets: academic, attendance, and disciplinary records. Teachers and staff now receive instant, actionable insights on each student’s dropout risk, empowering them to take prompt, targeted action.
Within the first year of launch, the district achieved:
reduction in dropout rates among middle school students. The first measurable decline in 5 years.
12%
decrease in student disciplinary actions
20%
more students reading on or above their grade level
27%
What We Did
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Unified Data Infrastructure
We built a centralized platform that combined:
1. Academic data from third-party assessment tools
2. Attendance records from PowerSchool database
3. Disciplinary history from client’s in-house SQL database
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Real-Time Student Watchlist & Alert System
We then combined the data streams into a single composite dropout risk score, making it possible to evaluate every student with one clear, consistent metric.
The dropout risk score is projected on a color-coded watchlist that updates daily, sending alerts to teacher and staff whenever a student’s risk score spikes, enabling same-day outreach.
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Predictive Analytics & Forecasting
Further down the timeline, we developed a predictive modeling engine that uses historical data to forecast student’s future dropout risk score 3 weeks in advance. The system leverages regression-based models tuned specifically to the district’s patterns.
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