Our latest eBook explores the topic of analytics programs specific to community colleges. It is an unfortunate truth that discussions of analytics and predictive modeling in higher education often neglect to include community colleges. This may be due, at least in part, to the fact that the majority of community colleges have an open-door admissions policy. Not requiring SAT scores or a specific high school GPA means they often have less robust data sets on incoming students than universities with stricter acceptance requirements.1 But the absence of specific data points does not mean analytics programs are not needed at community colleges, only that those data needs are different.
After all, more than a third of credit-seeking community college students are nontraditional (over 24 years of age), a stat which begs the question: do the high school grades of a 42-year-old professional looking to expand a skillset really tell us much about their likely success as an adult learner? Community colleges have a unique set of challenges and, like their four-year counterparts, data can help
address these challenges, boost student success, and support institutional goals. Follow along to learn six best practices for building and maintaining a community college analytics program.