Modeling Student Success of International Undergraduate Engineers

Teri Kristine Reed; P.K. Imbrie; Qu Jin; Joe J.J. Lin

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Conference: 2012 ASEE Annual Conference & Exposition
Location: San Antonio, Texas
Publication Date: June 10, 2012
Start Date: June 10, 2012
End Date: June 13, 2012
ISSN: 2153-5965
Conference Session: Global Engineering Models: Curriculum Development, Improvements, and Partnerships
Tagged Division: International
Page Count: 11
Page Numbers: 25.945.1 - 25.945.11
DOI: 10.18260/1-2--21702
Permanent URL: https://peer.asee.org/21702
Download Count: 589

Paper Authors

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Teri Kristine Reed Purdue University, West Lafayette orcid.org/0000-0002-6804-9826

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Teri Reed-Rhoads is Assistant Dean of engineering for undergraduate education, Associate Professor in the School of Engineering Education, and Director of the First-year Engineering program at Purdue University. She received her B.S. in petroleum engineering from the University of Oklahoma and spent seven years in the petroleum industry, during which time she earned her M.B.A. She subsequently received her Ph.D. in industrial engineering from Arizona State University. Reed-Rhoads’ teaching interests include statistics, interdisciplinary and introductory engineering, diversity, and leadership. Her research interests include statistics education, concept inventory development, assessment/evaluation of learning and programs, recruitment and retention, diversity, equity, and cultural humility. She has received funding from the National Science Foundation, Department of Education, various foundations, and industry. Reed-Rhoads is a member and Fellow of the American Society for Engineering Education and a member of the Institute of Electronics and Electrical Engineers and the Institute of Industrial Engineers. She serves as an ABET EAC Evaluator for ASEE.

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P.K. Imbrie Purdue University, West Lafayette

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P.K. Imbrie is an Associate Professor of engineering in the Department of Engineering Education at Purdue University. He holds B.S., M.S., and Ph.D. degrees in aerospace engineering from Texas A&M University. His research interests include educational research, solid mechanics, experimental mechanics, microstructural evaluation of materials, and experiment and instrument design. He has been involved with various research projects sponsored by NSF, NASA, and AFOSR, ranging from education-related issues to traditional research topics in the areas of elevated temperature constitutive modeling of monolithic super alloys and environmental effects on titanium based metal matrix composites. His current research interests include epistemologies, assessment, and modeling of student learning, student success, student team effectiveness, and global competencies; experimental mechanics; and piezospectroscopic techniques.

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Qu Jin Purdue University, West Lafayette

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Qu Jin is a graduate student in the School of Engineering Education at Purdue University. She received a M.S. degree in biomedical engineering from Purdue University and a B.S. degree in material science and engineering from Tsinghua University in China. Her research focuses on modeling student success outcomes, which include placement, retention, academic performance, and graduation.

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Joe J.J. Lin Purdue University, West Lafayette

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Abstract

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.

Citation
Format

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

TY  - CPAPER
AB  -                  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.
AU  - Teri Kristine Reed
AU  - P.K. Imbrie
AU  - Qu Jin
AU  - Joe J.J. Lin
CY  - San Antonio, Texas
DA  - 2012/06/10
PB  - ASEE Conferences
TI  - Modeling Student Success of International Undergraduate Engineers
UR  - https://peer.asee.org/21702
DO  - 10.18260/1-2--21702
ER  -