A Data-Informed Approach to Student Retention
Few institutions are performing sophisticated predictive modeling on factors affecting student retention, and given how many factors there are (both within and outside the institution’s control), predictive modeling can appear quite daunting. But you don’t have to go from A to Z all at once. “Typically,” Jim Scannell, president of Scannell & Kurz Inc, advises, “we encourage institutions not to leap into predictive modeling immediately. Start by doing univariate analysis, collecting descriptive knowledge.” For example, out of an entire class, you could set out to describe: How many men retain versus women? How many men versus women achieved higher than a 3.0 GPA? How did your transfer students from two-year institutions perform versus your transfer students from four-year institutions? Students from public versus private high schools? Student cohorts based on race? Suppose you find that men in that class are, on average, achieving a lower GPA than women. Can you dig deeper? For example, if you have strong athletics, compare both the academic preparation of entering athletes versus non-athletes and the academic success of those two groups during the first year. Do you have a lower GPA for male students because you enrolled 100 football players who were less academically […]