Data Mining: a Magic Technology for College Recruitment

This paper introduces a case study using data mining techniques to assist higher education institutions in achieving enrollment goals. The introduction includes the general life cycle of a data mining project: business understanding, data understanding, data preparation, modeling, assessment, and deployment. The model fit statistics show that data mining techniques, neural network, decision tree, logistic regression, and ensemble models, are successful in this project. Ensemble and neural network are better. The study concludes that data mining is an effective technology for college recruitment and also for institutional research and analysis. Data mining, defined as “the process of sampling, exploring, modifying, modeling, and assessing large amounts of data to uncover previously unknown patterns” (SAS, 2005, p. 1-3), has been used widely in various areas such as science, engineering, business, banking, and even combating terrorism for a long time. Its success and effectiveness in discovering actionable information from large sets of data has been well established. Recently, there has been an increasing trend that data mining techniques were used in institutional research and analysis including, but not limited to, college admissions yield prediction (Chang, 2006), retention and graduation prediction (Herzop, 2006; Sujitparapitaya, 2006; Baily, 2006), time-to-degree analysis (Eykamp, 2006; Herzog, 2006), enrollment management (Aksenova, Zhang, & Lu, 2006; Luan, 2006), course scheduling and online course offerings (Luan, 2006; Dai, Yeh, & Lu, 2007), student performance assessment (Dede & Clarke, 2007; Heathcote & Dawson, 2005; Minaei-Bidgoli, 2004; Ogor, 2007), and survey analysis (Yu, et. al, 2007). These applications have showed great success of various mining approaches in extracting information from the enormous higher education data. The findings help institutions of higher education make more effective decisions as to improve the quality of instruction and services. These studies also provide the evidence proving that data mining techniques have many advantages compared to the conventional analytical approaches in predicting and scoring behaviors of individual students (Luan & Zhao, 2006). However, the agreement about the significance of its practical application and evidence of its advantages still need to be further developed theoretically as well as practically. This paper, therefore, introduces a data mining project about college recruitment. With the evidence showing that attaining some level of education beyond high school is