Academic journal article Academy of Educational Leadership Journal

Estimation of General Education Program Enrollment

Academic journal article Academy of Educational Leadership Journal

Estimation of General Education Program Enrollment

Article excerpt

(ProQuest: ... denotes formulae omitted.)

INTRODUCTION

Course schedules are prepared and submitted by departments and schools well in advance of the start of the semester so that students are able to make plans for the timely completion of their academic degree programs. At this mid-western university, course schedules for the next fall semester are due at the beginning of November of the previous year, when admissions estimates, course pass rates, and student retention report are unavailable. However, the freshmen enrollment numbers stay relatively steady at around 3200, rarely differing by more than 100-200 students each fall. The schedules are available to students and advisors by the second week of November, early registration for continuing students begin in mid-March after spring break, spring grades are recorded in May, and freshmen and transfer student orientations begin in early June. Occasionally new instructors have to be hired to meet unexpected needs or current instructors have to have their schedules adjusted because of shifting demand. It is difficult for those instructors who are hired near the beginning of the semester to adequately prepare. It is also difficult for those students who are on a waiting list. Making precise predictions when preliminary schedules are constructed and adjusting these estimates as soon as possible are extremely important. Although complete relevant information on enrollment is not available at the time of prediction, an analysis of historical data makes it possible to construct generic course prediction models that are robust and fairly accurate for estimating the enrollment. This type of course prediction model can facilitate releasing additional seats to new students by better estimating seat requirement. New student registration is distributed over the summer preceding the fall semester through a series of sessions where students may register for courses. Universities use seat release systems to give similar enrollment opportunities to all incoming students. A seat release system also hedges fall course predictions by partially filling each section over time rather than filling each section in sequence. The model we present establishes the estimated demand for seats among three categories of General Education courses, namely Inner Core, Middle Core, and Outer Core courses. The model we present establishes the estimated demand for seats among three levels of General Education courses that were designed to be largely sequential, namely Inner Core, Middle Core, and Outer Core courses.

Enrollment prediction for general education courses, which provides information to the decision makers for budget planning and other aspects of planning, is important in many ways for the institution. Because of such importance, researchers have proposed many prediction methods to improve the accuracy of the enrollment estimation. However, obtaining accuracy on enrollment estimation is not an easy task, as many factors have impacts on the enrollment numbers. Many methods have been proposed and applied in enrollment prediction. Different models generate different results. The growth curve model by Weiler (1980) that was used for forecasting enrollment at the University of Minnesota, generated much variation in forecasting errors. Guo and Zhai (2000) applied survival ratio techniques to a four-year university enrollment. Song and Chissom (1993) applied the fuzzy time series approach to the enrollment prediction. Tsui & Murdock (1997) reviewed seven prediction models and analyzed the margin of errors on those models to comprehend the accuracy of the models.

As accuracy is an important concern in prediction, researchers engage in including more and more factors in their forecasting models. Some of the complex models combine the retention study and enrollment projection study together. These models also include such variables as high school and college grades, SAT or ACT scores, student demographic information, and their economic status. …

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