Academic journal article College and University

A Student-Tracking and Near-Future Student Enrollment-Forecasting Model

Academic journal article College and University

A Student-Tracking and Near-Future Student Enrollment-Forecasting Model

Article excerpt

This work describes the development of two important enrollment management tools: (1) a student-tracking model designed to monitor and report on student transitions through the educational experience, and (2) a near-term student enrollment-forecasting model. To illustrate how the models work, a fictional class of 500 freshmen and additional transfer students is followed from their initial matriculation in the Fall of 1997 through the Spring of 2003. From their aggregate experiences, the authors use their enrollment tools to forecast student enrollment for the Fall 2003 semester.

Institutions of higher learning, especially those whose operating budgets are tuition-dependent, devote considerable resources to the task of forecasting new freshman enrollment. While this work is crucial, the authors' models, which focus on the estimation of the next semester enrollments of currently enrolled students, previously enrolled students (stopouts or returning dropouts), and transfer students, offer colleges and universities a broader and more flexible way to manage enrollment.


Enrollment management specialists at institutions of higher learning are responsible for many activities that require accurate counts of various student groups (cohorts) and careful monitoring of student progress through the system (Hossler 1986). Some of the responsibilities of the enrollment management function include:

* monitoring matriculation and persistence (attrition) rates;

* anticipating trends and shifts in enrollment;

* setting enrollment goals and tracking progress toward those goals;

* predicting recruitment needs derived from graduation and attrition;

* managing the recruitment mix between freshmen and transfer students;

* avoiding enrollment shortfalls and excesses; and

* developing specific demographic targets for enrollment.

An appropriate studcnt-tracking/enrollment-forecasting model would enhance enrollment managers' ability to complete their tasks on a semester-by-semester rather than a yearly basis. Institutions would then avoid mid-year fiscal problems. Enrollment managers would also be able to focus on recruitment and the management of recruitment resources, including personnel, operating expenses, and financial aid requirements (Day 1997; Dennis 1998).

In the authors' previous work (Glynn and Miller 2002), a student-tracking model was developed that recorded student progress through college (student retention, attrition, and graduation rates) in enrollment management reports. The authors have supplemented that model with an important attribute-an algorithm for predicting near-future enrollments. The result is an enrollment-forecasting model guided by three desirable attributes: (1) simplicity (understandability), (2) utility (provide meaningful information), and (3) flexibility (easily modified).

The enrollment forecasts developed in this work are derived from data in the enrollment management reports of the first model. The outputs of the student-tracking model become the inputs to the enrollment-forecasting model. Therefore, much of the authors' student-tracking model will be included in this work.


For the purposes of this model, enrollment management terms are defined as follows:

* Freshman: enrolled student who has completed less than 30 credits.

* Sophomore: enrolled student who has completed 30-59 credits.

* Junior: enrolled student who has completed 60-89 credits.

* Senior: enrolled student who has completed 90 or more credits but has not yet graduated.

* Transfer Student: any student, who transfers in from another institution, is granted academic credit for coursework taken, and is expected to take less than eight semesters to graduate at the new institution.

* Persister: student who continues enrollment (does not drop out) from one semester to the next. …

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