Optimizing Course Offerings for Higher Ed

Why

Registrars at academic institutions know the students enrolled in the current program and their academic degree progress by the term. Many of the registrars also likely have a spreadsheet that can generate a rudimentary forecast of the students through the next few terms. But, this is often very static and very hard to change. Here are a few pressing questions: What is the forecast of the students through the next few terms? Which courses should a program offer? How can we optimize the course schedule offering? These are critical questions from a program chair or a college dean’s perspective.

They can plan their course offerings to meet the student demand while minimizing the academic operational costs and keep faculty and classrooms available consistent with the demand.

What

There are various datasets available that can help the tool builders and leadership teams. There is much to learn from the historical datasets and enrollment patterns. Using historical data, a modern library of forecasting models should be developed. The idea of having a collection of models is that it covers the variability in the historical time series exhibited by different courses in degree programs. Then there are academic situations and business processes such as course registration enrollment and dropout deadlines offered by institutions. Such valuable data sets can enrich the estimated students.

Going into specifics, most degree programs are sequential in nature. Students follow an advisor recommended path to degree completion. There are only handful paths to the degree completion. A historical trail of such paths taken by the students can be helpful in determining the student course needs for the upcoming term. Student flow forecasting models can provide academic advisors, registrars an aggregate view of expected students in the upcoming term. The projection of students needs in the upcoming term will serve as a critical input to a mixed integer programming model. The mixed integer programming model can then provide an optimal course offering plan for the upcoming term with a binary output such as whether to offer a core course or elective course.

Benefits

The benefits of such solution are obvious, it will maximize the student enrollment per section and classroom utilization and minimize the instructional cost by proactively identifying courses with low enrollment and not offering them. The faculty will also be able to maximize the dissemination of their course content to most students. Course planners, Degree program chairs who spend hours on determining the course offering for the upcoming term can reduce their planning time with such an automated solution.

Who:

Academic Operations Managers, Registrars for Higher Education will benefit most from such a solution. They

Interested in building such a tool? Write back to us at: scidstools@gmail.com. Your data is safe and secure with us. Student privacy, FERPA compliance will be enforced at all times.