Resumen de Pre-enrollment identification of at-risk students in a large engineering college
A. L. Kaleita, Gregory R. Forbes, Ekaterina Ralston, Jonathan Compton, Darin Wholgemuth, D. Raj Raman
Historical data from multiple institutions show that students who achieve a first-semester grade point average (GPA)below 2.0 are at substantially greater risk of leaving engineering programs before graduating with a degree than are thosewho achieved above 2.0. Identifying these ‘‘at risk’’ students prior to the start of their first semester could enable improvedstrategies to enhance their academic success and likelihood of graduation. This study used two distinct modelingapproaches to predict first-term GPA group (low-risk: GPA2.0; at-risk: GPA < 2.0) based upon data available priorto the student’s first pre-enrollment advising session. In the case of one of the approaches—which allowed a differentialweighting of Type I to Type II errors—we explore how these weightings influences the prediction accuracy. The modelsused academic and demographic data for first-year engineering students from 2010 to 2012 from a single large publicresearch-active institution. The two model types employed to build predictive models were (1) ordinary least squaresmultiple linear regression (MLR), and (2) classification and regression trees (CART). For both MLR and CART models,high school GPA and math placement exam scores were found to be significant predictorsof first-term GPA.Increasing thecost of missing at-risk students in the CART models improves at-risk prediction accuracy but also increases the rate of falsepositives (incorrectly identifying a low-risk student as at-risk). The relative simplicity of the CART models, as well as theease with which error-types can be weighted to reflect institutional values, encourages their use in this type of modelingeffort.
