This exploratory project will apply statistical classification analysis to college admission data to identify those variables that impact a student?s choice of engineering as the first and perhaps only major field of study. Variables, such as entrance exam scores, high school GPA, socio-economic status, ethnicity, gender, pre-engineering courses completed, and other factors that are suspected to influence an adolescent?s decision to study engineering, will be examined to determine their role in this decision. Specifically, statistical logistic regression, classification tree, and recently developed random forest techniques will be applied to a sample of students admitted to Texas Tech University. Once accomplished, these techniques will classify the variables and yield a predictive model for student recruitment to study engineering. These findings can then be used by the engineering community to design courses, curricula and recruitment programs that both attract students and also introduce students to the study of engineering in a manner more consistent with their expectations and abilities.
Improved recruitment and retention strategies will help increase the number of engineering graduates needed by the nation?s workforce, and the results of this project will be a resource for guidance about recruiting students to engineering programs. This project will apply recently developed statistical classification techniques to a large data sample and can filter through many variables simultaneously and identify the significance of each variable. General recruitment of engineering students can be improved by taking advantage of this additional insight into why students select engineering prior to entering college. Using these results efforts and initiatives directed towards increasing the number of students enrolling in engineering can be focused on to the significant decision variables. The findings will also be important for college recruiters as they design and implement engineering student recruitment programs.
