How Predictive Analytics is Transforming Higher Education

The pressure on higher education institutions regarding student retention is reaching a tipping point. Federal and state officials are starting to require that students who enter public institutions earn their degree, especially if they represent a minority group.

More than two dozen states offer funding based on how many students an institution graduates, not how many it enrolls. The cost of recruiting and educating students also is continuing to rise, making student retention even more crucial to the bottom line. As costs increase, colleges hope to overcome this challenge by balancing the resources they use to recruit students with revenue generated when those students are retained.

To help achieve this, institutions are using predictive analytics to analyze demographic and performance data to predict whether a student will enroll at an institution, stay on track in their courses or require support so they don’t fall behind.

Using predictive analytics in this way makes it possible for institutions to meet their annual enrollment and revenue goals with more targeted recruiting and strategic use of financial aid. Additionally, predictive analytics allows colleges to tailor their advising services and personalize learning to help improve student outcomes.

Predictive Analytics in Higher Ed

Institutions in higher education are using predictive analytics as a way to respond to the many business and operational changes happening in the education industry. Below are the three main reasons colleges are using this tool:

Targeted Student Advising

Few colleges have an adequate number of advisors on staff, and as a result, students often cannot receive the individualized attention they need. A survey conducted by the National Academic Advising Association (NACADA) found that the national average caseload of advisees per full-time professional academic advisor was 296-to-1. The ratio jumped to 441-to-1 at community colleges.

However, systems based on predictive analytics like early-alert and program recommender can help identify students who are in need of support and allow staff and faculty to assist.

Adaptive Learning

Colleges are also using predictive analytics to develop adaptive learning courseware, which is designed to modify a student’s learning route based on their interactions with the technology. Using predictive analytics in adaptive learning platforms can help instructors pinpoint students’ learning gaps and then customize the academic experience so it better aligns with how students learn.

This tool helps students accelerate their learning by allowing them to quickly go through content they already know, while providing additional support in areas where they struggle.

Manage Enrollment

Colleges are also using predictive analytics to better inform enrollment management plans. This information helps schools forecast the size of incoming and returning classes. It is used to help the school narrow the focus of their recruitment and marketing efforts so they are only targeting students who are most likely to apply, enroll and succeed. Predictive analytics helps colleges anticipate the financial need of incoming and returning classes to determine whether a student will accept the financial aid award offered to them.

Approaching Predictive Analytics Ethically

Analyzing personal student data using predictive analytics requires careful attention to ethics and privacy.

According to The Atlantic, the best predictive models avoid making recommendations based solely on a student’s financial or cultural background. Because structural inequality makes up so much of the world, students with low-income backgrounds, first-generation students, and students of color tend to graduate with college degrees at much lower rates when compared to affluent white students.

Therefore, when institutions use predictive analytics to look at race, ethnicity, age, gender, or socioeconomic status to determine which students to target for enrollment or intervention, they can intentionally or unintentionally reinforce that sense of inequality.

For colleges and universities that are just learning how to use analytics to make decisions, remaining ethical in their use of student data can be a struggle, which is why a high degree of training and security is necessary.

Predictive Analytics Case Study

Georgia State University is known as a leader in leveraging data to provide individualized attention to students who need it most. Higher Education Marketing reports that in the last decade, Georgia State has tracked more than 140,000 student records and 2.5 million grades to identify 800 different factors that put students at risk of dropping out. Some of these risk factors include enrolling in the wrong course for their major or low grades in an introductory course needed for a particular major.

When any one of these academic mistakes occur, an alert is triggered in their early-warning system. A one-on-one student intervention is initiated within 48 hours of the alert. More than 51,000 interventions were conducted in 2016. Georgia State also added dozens of academic advisors, centralized operations and information sharing, and expanded on current resources such as peer tutoring. As a result of these changes, graduation rates have increased over the last decade by 22%, and students are completing their degrees half of a semester sooner on average.

The school has seen the most improvement with at-risk minority, first-generation and non-traditional students, who were previously falling through the cracks. The insights gained from their predictive models enabled them to anticipate students at financial risk and student demand for specific courses, which helped make scheduling processes more efficient.