Using predictive modeling to determine fall and spring semester enrollment. This requires two sets of models that must be joined together. The first set will focus on the predicted retention of currently enrolled students and the second set will focus on the enrollment of new applications.
Evaluation Plan and Measures: The number of models completed and comparison to corresponding semester enrollment projects.
KPIs: Proportion of retention and applicant enrollment models completed.
Baseline measure (for each KPI): No models have been completed at this time.
Current/most recent data (for each KPI) [NEW for 2023]: 0% of the five models completed.
Goal or targets (for each KPI): All models completed by the end of Fall 2023.
Time period/duration: End of Fall 2023 semester
The underlying datasets for the retention models have been combined and model development is underway. Once the models are developed, the recent classes will be scored for probability of retention, then those probabilities will be compared to students’ actual enrollment for the respective semesters. Also, a similar process needs to be completed for Application data.
Continue to develop the models for fall-to-fall, fall-to-spring, and spring-to-fall retention along with the fall and summer applicant enrollment models.
