Predictive analytics in higher education
Institutions are turning to Predictive Analytics to increase their bottom line and competitive advantage. In order for educational institutions to differentiate themselves and remain competitive, institutions must identify and understand the needs of their students. Traditional surveys are retrospective and only offer an insight after the fact. Real-time data like attendance and grades provide current and future insights.
Institutions can use predictive analytics to identify completion rates, student success after graduation, and even personalise the learning process for students.
There are numerous applications of predictive analytics in Higher Education. Below are the three main reasons universities and colleges are using Predictive Analytics:
Efficient student advising
Advisor caseloads are growing year on year.
In the US, The National Academic Advising Association (NACADA) found that the national average caseload per full-time professional academic advisor was 296-to-1. The ratio jumped to 441-to-1 at community colleges.
Assigning advisors efficiently and automating communications can greatly reduce the caseload on an individual level.
Early Alert Systems identify at-risk students as early as the first semester and allocate appropriate advisors based on the student’s needs using predictive analytics, and strategic workflows.
Finding students who are most likely to graduate is another application of predictive analytics in Higher Education. Student data helps schools forecast the size of incoming and returning classes. Institutions can also narrow their recruitment and marketing efforts so they are only targeting students who are most likely to apply, enroll and succeed. Predictive analytics helps Institutions anticipate the financial needs of incoming and returning classes to determine whether a student will accept the financial aid award offered to them.
Predictive Analytics can be used to modify a students learning route based on their interactions to maximise student success. A one-size-fits-all approach is no longer the only option with instructors now capable of pinpointing students’ learning gaps and customising the academic experience so it can better align with how the student learns.
What is your preferred application of predictive analytics in Higher Education?
Where would you like to apply predictive analytics in Higher Education?
Let us know in the comments!