Collect data at a granularity that permits risk score creation for each enrolled student
A community college providing open access to quality higher education was looking for a way of predicting student retention risk so they could intervene and help at-risk students succeed.
The college’s student retention rate over 6 years was below 50 percent. They knew they needed to find proactive ways of identifying at-risk students, but weren’t sure how to best leverage their data to do it. The college’s Institutional Research team brought in Ironside to test predictive modeling techniques, evaluate and select a set of predictor variables on which to base student risk scores, segment students into groups based on GPA attainment and retention likelihood, integrate with the college’s student services model, data warehouse, and BI platform, and output individualized student risk scores and intervention/retention strategies for at-risk students.
The Ironside Data Science & Advanced Analytics team is dedicated to helping organizations realize the full potential of their data resources. The client brought in the team to do the following:
Define the Vision
Prepare the Data
Develop & Refine Scoring Methods
Ensure Future Progress
KEYS TO SUCCESS
Through predictive analysis, the client was able to: