Predictive Analytics for Student Success
AI/ML platform for predictive analytics and personalized learning recommendations to improve student retention and success.
The Challenge
The district needed to improve student retention and success rates using data-driven insights.
- Low student retention rates
- Limited data on student performance
- Manual intervention processes
- No predictive analytics capability
- Faculty overwhelmed with data
- Lack of personalized learning tools
Our Solution
Developed and deployed an AI/ML platform for predictive analytics and personalized learning.
Predictive Analytics Engine
Machine learning models for student success prediction
Machine LearningPredictive Analytics
Data Visualization
Dashboards for faculty and administrators
Data VisualizationDashboards
Learning Platform Integration
Personalized learning recommendations
Learning PlatformPersonalization
Implementation Process
Total Timeline: 10 months
- Phase 1: Data Collection & Modeling (3 months)
- Data collection and cleaning
- Model development
- Faculty workshops
- Phase 2: Platform Deployment (4 months)
- Platform integration
- Dashboard deployment
- Faculty training
- Phase 3: Optimization & Scaling (3 months)
- Model optimization
- Scaling to all campuses
- Ongoing support
Measurable Results
Increased student retention, improved course completion, and high faculty satisfaction.
Student Success Outcomes
- 25% increase — Student retention
- 35% improvement — Course completion
- 80% — Early intervention success
- 92% — Faculty satisfaction
Key Technologies
Machine LearningPredictive AnalyticsData VisualizationLearning Platform
“The predictive analytics platform has transformed how we support students. Retention and success rates are at an all-time high.”
Dr. Lisa Brown
Dean of Student Success, Community College District
Next Steps
Expand predictive analytics to new programs and integrate with career services.