Using Business Intelligence in College Admissions: A Strategic Approach

Using Business Intelligence in College Admissions: A Strategic Approach

W. O. Dale Amburgey (Saint Joseph’s University, USA) and John Yi (Saint Joseph’s University, USA)
Copyright: © 2011 |Pages: 15
DOI: 10.4018/jbir.2011010101
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Higher education often lags behind industry in the adoption of new or emerging technologies. As competition increases among colleges and universities for a diminishing supply of prospective students, the need to adopt the principles of business intelligence becomes increasingly more important. Data from first-year enrolling students for the 2006-2008 fall terms at a private, master’s-level institution in the northeastern United States was analyzed for the purpose of developing predictive models. A decision tree analysis, a neural network analysis, and a multiple regression analysis were conducted to predict each student’s grade point average (GPA) at the end of the first year of academic study. Numerous geodemographic variables were analyzed to develop the models to predict the target variable. The overall performance of the models developed in the analysis was evaluated by using the average square error (ASE). The three models had similar ASE values, which indicated that any of the models could be used for the intended purpose. Suggestions for future analysis include expansion of the scope of the study to include more student-centric variables and to evaluate GPA at other student levels.
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Study Design And Methodology

The primary methodology of the study consists of analysis of historical student data to determine the best-fit model to predict applicants’ end-of-first-year GPA. Three types of analytical models will be developed, and comparison testing will be conducted to determine the model displaying the lowest error.

Data stewards of the institution representing the Office of the Registrar, the Office of Financial Assistance, and the Director of Enrollment Analysis conferred to develop standards of acceptable use of the historical data. All agreed that the potential results of the study were significant enough to justify use of the data and that the study had to strive to protect the anonymity of the student data.

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