Last updated: December 1st, 2020
Each year at academic institutions across the country, first-year students pack their bags, leave their dorms, and never return to that college or university. For some, the school wasn’t the right fit. For others, some combination of course load, difficulty adjusting, paying tuition, and other personal factors causes more stress than they can handle. Whatever the case, students are leaving, and institutions are losing valuable scholarship, tuition, and onboarding revenue with every student who decides to leave. Student churn is detrimental to students and universities alike.
Churn rates take into account much more than whether a single student leaves a university. When researchers analyze churn rates, they explore soft drops (classes, majors) and hard drops (entire universities). They also look at things such as demographics, course load, course combinations, and socioeconomic factors.
Analyzing the Data
Data published in 2019 by the National Student Clearinghouse Research Center demonstrates that universities retained between 69 and 73 percent of their full-time students over an eight-year period. The retention numbers drop to between 39 and 46 percent for part-time students and average between 59 and 62 percent across the entire student population. These figures mean that between 27 and 54 percent of students leave the first university of their choosing, never to return.
A deeper dive into the data reveals that race and ethnicity play a significant role worth examining. The Clearinghouse looked at students who entered university in the fall of 2017 and reported on returning students’ races. Universities retained 72 percent of Asian students, 62 percent of White students, 59 percent of Hispanic students, and 52 percent of Black [sic] students. The factors that contribute to these statistics can be found in the data, analyzed and modeled to alert us to who is at risk.
Applying Predictive Analytics
Predictive algorithms can automate the analytical process and proactively point administrators to students who might be at risk of leaving the university before they actually depart. By examining demographic, performance, and logistical data, we can forecast the likelihood of a given student succeeding at a given university and develop intervention plans for those at risk. This analysis not only helps the university forecast tuition revenue and plan administrative expenses, but can also find the students who are lost on the university trail and put them back on the path to success.
The COVID-19 pandemic has brought additional complexity to the problem of student churn. Specifically, it has caused concern about a higher education bubble that may be poised to pop. Advanced data and analytics platforms can examine remote learning data points, contrast them with the on-campus experience, and provide insights that can ensure the right mix, empowering student engagement that matches the tuition value proposition.
With the power of an enterprise data analytics platform, university departments can keep track of vast volumes of data and identify what is most relevant at a given point in time. This data provides the whole institution with incredible insight beyond just student churn, such as where to hold fundraising events, how to schedule courses, and even when to hold departmental meetings. The clarity and precision provided by enterprise data analytics combined with predictive modeling drives efficiencies that equally benefit students, faculty, and administrators. Implementing such technology is a win for everyone.