San Antonio, Texas
June 10, 2012
June 10, 2012
June 13, 2012
25.945.1 - 25.945.11
Modeling Student Success of International Undergraduate EngineersModeling student retention using entering secondary school academic performance metrics only islimited at best. Past research has shown that these variables can be somewhat informative, but are notthe whole story. In order to expand our understanding of successful students, defined in this study asstudents who are retained and ultimately graduate with a degree in engineering, student retention andgraduation modeling has been extended to include not only secondary school academic performance,but also self-reported affective measures. The Student Attitudinal Success Instrument (SASI), a 161-itemsurvey assessing 13 specific noncognitive constructs, was developed based largely on existinginstruments. This SASI is designed to provide data on noncognitive characteristics for incomingengineering students (a) prior to the onset of the first year and (b) for which higher educationinstitutions may have an influence during students’ first year. Data collected from this instrument havebeen found to be suitable for use in the development of predictive models of student retention and/orgraduation, which is the definition of success in this model. The SASI is used to provide informationabout the academic preparation and affective characteristics of incoming first-year engineeringstudents. Such systematically gathered information helps us assess the impact of University andprogrammatic decisions aimed at student recruitment, admission, retention, and ultimately the successof all students and, in particular, minority student populations.Though international students in engineering tend to have higher levels of overall retention andgraduation versus any other majority or minority population, this study shows that the trend is in aconcerning downward direction. In order to reverse this graduation trend, programs need to beexpanded or created that are based on informed data decisions specific to student populations, such asinternational students. Understanding additional measures beyond admission metrics that lead tostudent success allows policy, programs or programmatic changes that increase overall student success.This study begins with a review of how this type of modeling was used to inform a change in admissionspolicy in the case of gender bias. Then, the techniques are expanded to international student successmodeling. Though international and domestic students report similar levels of each success measure,the relative importance of each measure in predicting retention was different for these two studentpopulations.
Reed, T. K., & Imbrie, P., & Jin, Q., & Lin, J. J. (2012, June), Modeling Student Success of International Undergraduate Engineers Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21702
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