RPI ID: 2021-021-401

Innovation Summary:
A predictive analytics system uses student data to forecast retention rates across academic programs. The model incorporates behavioral, demographic, and performance metrics to identify at-risk students. Machine learning algorithms continuously refine predictions based on new inputs. The system supports institutional decision-making and targeted interventions.

Challenges / Opportunities:
Student retention is a key metric for educational institutions, yet predicting it remains difficult. This invention provides a data-driven approach to identify patterns and risk factors. It enables proactive support strategies and resource allocation. The system can be integrated into existing student information platforms.

Key Benefits / Advantages:
✔ Predictive modeling of retention
✔ Data-driven student support
✔ Continuous learning algorithms
✔ Institutional planning tool
✔ Scalable across programs

Applications:
• Higher education analytics
• Student success programs
• Institutional planning

Keywords:
#educationanalytics #studentretention #machinelearning #predictivemodeling #academicperformance #edtech

Intellectual Property:
US Application 18/835804 US20250131520A1 filed 05-Aug-2024