Predicting Post Secondary Educational Outcomes With Survival Analysis
- Conference
- Location
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Austin, Texas
- Publication Date
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June 14, 2009
- Start Date
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June 14, 2009
- End Date
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June 17, 2009
- ISSN
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2153-5965
- Conference Session
- Tagged Division
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Educational Research and Methods
- Page Count
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16
- Page Numbers
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14.966.1 - 14.966.16
- DOI
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10.18260/1-2--5747
- Permanent URL
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https://peer.asee.org/5747
- Download Count
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1589
Paper Authors
Gillian Nicholls University of Pittsburgh
Gillian Nicholls is a Lecturer in Industrial Engineering at the University of Pittsburgh. Her research interests are in applying statistical analysis and optimization to engineering education and transportation management. She holds the B.S. in Industrial Engineering (Lehigh University), Masters in Business Administration (Penn State University), and M.S. in Industrial Engineering and Ph.D. in Industrial Engineering(University of Pittsburgh.) Address: 1048 Benedum Hall, University of Pittsburgh, Pittsburgh, PA
15261; telephone 412.400.8631; fax: 412.624.9831; e-mail: gmn3@pitt.edu.
Harvey Wolfe University of Pittsburgh
Harvey Wolfe is the William Kepler Whiteford Professor of Industrial Engineering at the
University of Pittsburgh. After many years working in the area of applying operations research methods to the health field, he is now active in the development of models for assessing
engineering education. He is a co-author of Engineering Ethics: Balancing Cost Schedule and
Risk - Lessons Learned from the Space Shuttle (Cambridge University Press, 1997). He holds the
B.E.S. in Industrial Engineering, M.S.E. in Operations Research, and Ph.D. in Operations
Research (Johns Hopkins University).
Mary Besterfield-Sacre University of Pittsburgh
Mary Besterfield-Sacre is an Associate Professor of Industrial Engineering and the Fulton C.
Noss Faculty Fellow at the University of Pittsburgh. Her research interests are in engineering education evaluation and empirical and cost modeling applications for quality improvement in manufacturing and service organizations. She holds the B.S. in Engineering Management (University of Missouri Rolla), M.S. in Industrial Engineering (Purdue University), and Ph.D. in Industrial Engineering (University of Pittsburgh).
Larry Shuman
University of Pittsburgh
orcid.org/https://0000-0001-6884-7070
Larry J. Shuman is Associate Dean for Academic Affairs, School of Engineering, University of Pittsburgh and Professor of Industrial Engineering. His research includes studies directed at the undergraduate engineering learning experience, assessment and the ethical behavior of engineers. Dr. Shuman has published widely in the engineering education literature and is a co-author of Engineering Ethics: Balancing Cost Schedule and Risk - Lessons Learned from the Space Shuttle (Cambridge University Press, 1997). He holds the B.S. in Electrical Engineering (University of
Cincinnati) and Ph.D. in Operations Research (Johns Hopkins University).
Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract
Predicting Post-Secondary Educational Outcomes with Survival Analysis Survival Analysis, STEM, NELS
Abstract
Identifying potential students and understanding what affects their decision to depart the track of obtaining a college degree in engineering is critical to engineering educational research. This study used data from the National Education Longitudinal Study of 1988-2000 (NELS) to develop a model for predicting post-secondary educational outcomes with particular focus on students earning a college degree in Science, Technology, Engineering, or Mathematics (STEM).
on the STEM track past a key time point in the study at which most students attending college were nearing graduation. NELS provided a comprehensive set of variables for 12,144 students of which 11,128 had clearly determinable educational outcomes. The set of potential outcomes included dropping out of high school, completing education at high school graduation, dropping out of college, earning a less than four year degree, earning a degree other than STEM, earning a
utilized demographic, attitudinal, and academic performance data mainly collected at the 8th grade level as well as standardized test scores and college enrollment status variables.
Survival analysis models were fit with randomly selected data and then applied to reserved test data to The models performed well in distinguishing between STEM students and those who did not successfully complete a college degree. The different categories of educational outcomes exhibited markedly different hazard curves showing the periods of greatest STEM track departure risk varied between student outcomes. This suggested that the optimum times at which to offer positive interventions to keep students on the potential STEM track vary by the type of outcome they are otherwise likely to experience. This includes encouraging students to remain in high school, to apply to college, and to persist once enrolled in an STEM program.
Introduction
The process of educating students from junior high school through college is of vital importance to the field of engineering education. Producing a sufficient number of engineering graduates depends directly upon the number and quality of students that enter college and select engineering as a major. Students that are lost to engineering by dropping out of high school; choosing not to pursue a college degree; dropping out of college; or switching out of engineering e of these students would not have ultimately earned an engineering degree due to greater interest in other fields of study, but others might have persisted to graduation with greater preparation, encouragement, and engagement. This study examines the factors that predict whether or not a student will persist to graduate college with a degree in Science, Technology, Engineering, or Math (STEM).
Nicholls, G., & Wolfe, H., & Besterfield-Sacre, M., & Shuman, L. (2009, June), Predicting Post Secondary Educational Outcomes With Survival Analysis Paper presented at 2009 Annual Conference & Exposition, Austin, Texas. 10.18260/1-2--5747
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