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Main Menu Session Number 2557

Modeling for Educational Enhancement and Assessment*

Mary Besterfield-Sacre1, Larry Shuman1, Harvey Wolfe1, Alejandro Scalise 2, Siripen Larpkiattaworn 1, Obinna S Muogboh1, Dan Budny 1, Ronald Miller3 and Barbara Olds3 1 University of Pittsburgh/ 2TransSolutions/3Colorado School of Mines

Abstract Industrial engineering programs have typically adopted the new ABET accreditation criteria with more enthusiasm than other engineering programs, in part since the principles of continuous improvement and statistical measurement are commonly taught in most curriculums, and skills such as team work and data analysis are staples of modern IE curricula. However, such complementary skills should not limit the expertise that industrial engineers use to improve engineering programs. Mathematical models can be effective tools for both enhancing learning and assessment. This paper presents a number of modeling approaches that a team, consisting primarily of industrial engineers at the University of Pittsburgh has developed in conjunction with colleagues at the Colorado School of Mines over the course of several years to demonstrate the efficacy of this approach to ABET’s requirement of continuous improvement. Using both logistic regression analysis and various neural network algorithms, we have employed empirical modeling to successfully improve retention in engineering, predict probation during the first year, and determine proper placement in math courses. We are also in the early stages of developing similar models to determine a student’s intellectual development, determine student achievement based on students’ attitudes towards engineering and themselves, as well as predict various EC 2000 outcomes based on students’ attitudes. We describe each of theses models separately in this paper to emphasize the need for modeling as a viable tool for evaluation in engineering education.

Introduction The Accreditation Board for Engineering and Technology’s (ABET) performance-based criteria, “EC 2000,” require that each engineering program’s faculty implement and maintain a closed- loop, continuous improvement system [1]. As part of that system, faculty must demonstrate that the program’s graduates have, in fact, acquired certain knowledge and skills including a minimum set of eleven outcomes. In addition, the system must be flexible enough to allow for the continuous identification of areas for improvement and the ability to measure resultant improvements. Understanding the direct and indirect relationships among student attributes and outcomes is crucial because such knowledge can provide the foundation for continuous improvement in engineering education and a key to realizing the promise of the new ABET criteria. Industrial engineering departments possess and teach many of the skills necessary to be successful in the new ABET perspective, specifically statistics and quality management techniques. This paper focuses on another set of valuable skills – that of empirical modeling, which can be employed to achieve the objectives of the new accreditation criteria.

* This paper supported in part by National Science Foundation grant: EEC-9872498, Engineering Education: Assessment Methodologies and Curricula Innovations, DUE-0121520, Engineering Education: Assessment Methodologies and Curricula Innovations II, and Engineering Information Foundation grant EiF 98-4.

Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition Copyright Ó 2002, American Society for Engineering Education

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Larpkiattaworn, S., & Muogboh, O., & Besterfield-Sacre, M., & Shuman, L., & Scalise, A., & Budny, D., & Olds, B., & Miller, R., & Wolfe, H. (2002, June), Modeling For Educational Enhancement And Assessment Paper presented at 2002 Annual Conference, Montreal, Canada. 10.18260/1-2--10208