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Building Predictive Models Using Sas ® Enterprise Miner
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2017 |
| Abstract | Given the proposed budget cuts to higher education in the state of Kentucky, public universities will likely be awarded financial appropriations based on several performance metrics. The purpose of this project was to conceptualize, design, and implement predictive models that addressed two of the state's metrics: six-year graduation rate and fall-to-fall retention for freshmen. The Western Kentucky University (WKU) Office of Institutional Research analyzed five years' worth of data on first time, full time bachelor's degree seeking students. Two predictive models evaluated and scored current students on their likelihood to stay enrolled and their chances of graduating on time. Following an ensemble of machine-learning assessments, the scored data were imported into SAS® Visual Analytics, where interactive reports allowed users to easily identify which students were at a high risk for attrition or at risk of not graduating on time. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.wku.edu/instres/documents/wku_ir_examining_higher_ed_performance_metrics_sas_miner_va.pdf |
| Alternate Webpage(s) | http://support.sas.com/resources/papers/proceedings17/0788-2017.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
