The ability to adapt to changing trends and ensure success is paramount in any business and more important than ever in higher education as there is an increased focus on ensuring student success. To achieve this, many institutions are turning to predictive analytics. In this blog post, we will explore the use of predictive analytics in forecasting enrollment trends, identifying at-risk students, and making data-informed decisions.
Predictive Analytics
Predictive analytics is a method that harnesses historical data, statistical algorithms, and machine learning to make informed predictions about future events. In higher education, this means leveraging data to anticipate enrollment trends, identify students who may be at risk of not succeeding academically place resources where they should be to help those students, and ultimately enhance decision-making processes.
Forecasting Enrollment Trends
One of the key areas where predictive analytics shines is in forecasting enrollment trends. For a university like Marshall University, understanding how many students to expect in the coming semesters is vital for resource allocation and planning. Predictive models can analyze historical enrollment data, considering various factors such as demographics, economic conditions, and academic program popularity, to provide accurate enrollment predictions. I have begun efforts to develop such a model at Marshall in a proactive approach that all institutions should take in today’s competitive educational landscape.
Identifying At-Risk Students
Beyond enrollment predictions, predictive analytics can also help institutions identify students who may be at risk of not completing their degrees. By analyzing academic performance, attendance records, and other relevant data, predictive models can flag students who might benefit from additional support or interventions. This early identification allows universities to provide targeted assistance and improve student outcomes, ultimately contributing to higher retention and graduation rates.
Data-Informed Decision-Making
The power of predictive analytics does not stop at enrollment and retention. It extends to informing strategic decisions across the institution. Whether it’s optimizing course scheduling, allocating resources efficiently, or tailoring academic programs to meet student demand, data-driven decision-making has become a cornerstone of modern higher education administration.
Conclusion
By leveraging historical data specific to an institution’s context, one can develop a model that not only predicts enrollment but also factors in the university’s unique attributes. Predictive analytics is revolutionizing higher education by providing valuable insights into enrollment trends, student success, and data-informed decision-making. If you are just getting started, like I am, as you continue to refine your model and embrace data-driven strategies, you will place your university in a noble position to thrive in the ever-changing landscape of higher education. Predictive analytics is not just a tool; it’s a pathway to a more resilient and successful future for both the university and its students.
To predicting the future at your institution with data!
Brian M. Morgan
Chief Data Officer, Marshall University