The Client
The client is a Community College District aiming to leverage data-driven insights to boost student retention and success rates across its campuses. They needed a powerful analytics platform to move from reactive, manual interventions to proactive, personalized student support.
Industry
Education
Tech Stack
Machine Learning, Predictive Analytics, Data Visualization, Learning Platform Integration
The Challenges
The district needed to improve student retention and success rates but was hindered by a lack of actionable, data-driven insights.
- Persistently low student retention rates were impacting the institution's goals.
- There was limited, fragmented data available on key indicators of student performance.
- Student support and intervention processes were entirely manual and reactive.
- The district lacked any predictive analytics capability to identify at-risk students early.
- Faculty and administrators were overwhelmed with raw data they couldn't effectively use.
- There was a significant lack of personalized learning tools to support individual student needs.
Solutions We Offered
JadeQuest developed and deployed a sophisticated AI/ML platform designed to provide predictive analytics and personalized learning recommendations, empowering faculty and staff to intervene proactively.
Predictive Analytics Engine
We built a powerful engine using machine learning models to analyze historical and real-time student data. This engine accurately predicts student success outcomes, identifying individuals at risk of falling behind or dropping out and visualizing the data for easy interpretation.
Dashboards for Faculty and Administrators
Intuitive, role-based dashboards were created to provide faculty and administrators with actionable insights. These visualizations highlighted at-risk students, tracked performance trends, and allowed for effective monitoring of intervention strategies.
Learning Platform Integration
The platform was seamlessly integrated with the district's existing learning management system (LMS). This allowed for the delivery of personalized learning recommendations and resources directly to students based on their individual performance and predicted needs.
Implementation Process
The entire project was completed in a 10-month timeline across three phases. The first phase, Data Collection & Modeling (3 months), involved gathering and cleaning data to build the predictive models. The second phase, Platform Deployment (4 months), focused on integrating the platform and deploying the dashboards. The final phase, Optimization & Scaling (3 months), involved refining the models and scaling the solution to all campuses.
Results
The platform led to a marked increase in student retention, improved course completion rates, and received high faculty satisfaction by providing timely and actionable data.
Student Success Outcomes
25% increase in overall student retention rates.
35% improvement in course completion rates for at-risk students.
80% success rate for early interventions triggered by the platform.
92% faculty satisfaction with the platform's usability and insights.
