Even with extensive retention research dating from the 1960s, community colleges still struggle to identify the reasons why students do not return to college. Data mining has allowed these retention models to evolve to identify new patterns among student populations and variables. The purpose of this study was to create a predictive model for…

This study seeks to extend the literature on higher education enrollment patterns during times of recession by examining patterns of enrollment and successful course completion in one of the world's largest higher education systems--the California Community College system. The data are drawn from publicly available data sources on the web. CCC…

The authors describe an innovative technique for using Classification and Regression Tree (CART) with student characteristics, including geographical data for analyzing student enrollment patterns between students who enroll within a community college district (stayers) and those who enroll outside the district (movers) at neighboring community…