A Method For Predicting Post Secondary Educational Outcomes
- Conference
- Location
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Pittsburgh, Pennsylvania
- Publication Date
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June 22, 2008
- Start Date
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June 22, 2008
- End Date
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June 25, 2008
- 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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14
- Page Numbers
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13.55.1 - 13.55.14
- DOI
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10.18260/1-2--4301
- Permanent URL
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https://peer.asee.org/4301
- Download Count
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373
Paper Authors
Gillian Nicholls University of Pittsburgh
Gillian Nicholls is a Ph.D. candidate 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 (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
A Method for Predicting Post-Secondary Educational Outcomes Abstract
Identifying potential engineering students and understanding what affects their choice of college major is critical to engineering educational research. Insufficient numbers of students are majoring in Science, Technology, Engineering, or Mathematics (STEM) topics. Understanding the factors that affect students’ interest in studying STEM, capability of succeeding in STEM, and likelihood of persisting to achieve a STEM degree is of vital concern to educators.
This study used an extensive national longitudinal dataset of over 12,000 students to develop a set of logistic regression models for predicting which students ultimately achieve a STEM degree vs. another educational outcome. The potential educational outcomes included no college degree, a less than four year college degree, a Non-STEM college degree, a STEM college degree, and a newly proposed category of STEM-Related college degree. Another model comparing the probability of STEM vs. all the other possible outcomes combined was also constructed. The resulting models demonstrated strong predictive accuracy in discriminating between a STEM degree and an alternative educational outcome. The predictive accuracy of the models was examined with Receiver Operating Characteristic (ROC) Curves. Several measures of student academic capability, prior academic performance, attitudes, experiences, and family influences were consistently found to be statistically significant predictors of STEM.
Introduction
The progression of students through the American educational system from kindergarten to acquisition of a college degree is a lengthy process. At present, the quantity of students that complete degrees in Science, Technology, Engineering, and Math (STEM) is not sufficient1. The volume of undergraduates enrolled from 1992 to 2004 increased steadily2. However, the pattern of degrees earned from 1996 through 2005 indicates that only small increases have occurred in the bachelors, masters, and doctoral degrees achieved by U.S. citizens and permanent residents. The number of mathematics degrees earned during this period also exhibited little growth. The volume of full-time graduate students increased but the number earning advanced degrees in other science topics exhibited slight increases or declines through 20013.
Producing STEM degree-holders is a process that depends upon the students, educators, and the means by which students are educated. The students are a vital portion of the raw materials to this process and issues that affect their quantity and quality also affect the resulting number of degree-holders. Studying this process in order to identify significant factors that affect the production of degree-holders could provide a guide towards improving the process. A methodology to test the effect of these factors could aid in designing an intervention program to encourage and assist more students in pursuing a college degree in STEM.
Developing such a methodology starts with examining the work of education researchers who have explored the motivations of students and the predictors of student success in school. Variables found by other education researchers to have been significant predictors of STEM interest, persistence, and successes are a natural starting point for this analysis.
Nicholls, G., & Wolfe, H., & Besterfield-Sacre, M., & Shuman, L. (2008, June), A Method For Predicting Post Secondary Educational Outcomes Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--4301
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