Enrollment modeling has many facets, largely: the educational pipeline, student enrollment behaviors while enrolled at the institution, and degree awards. Dr. Archer has many years of experience in helping universities gain a better understanding of the complexities of enrollment modeling on their campuses. Dr. Archer has advised many institutions on how to implement such techniques over the years, and have been presented this topic at numerous professional conferences.
Modeling Performance Metrics
Dr. Archer has worked with developing models for costing, institutional production and efficiencies, student success metrics, and other key performance indicators. The first step in creating prediction models is to understand the correlations that exist within the data. Once these correlations are quantified, the system model can help decision makers better understand the expected outcomes of implementing policy in terms of impact on performance metrics.
Predictions and forecasting
Sometimes in the absence of correlational data, trend forecasting is the best method for creating predicted future outcomes. Time series techniques can be applied to past and current data trends in order to help predict expected data trends in the future.