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Understanding Grader Reliability through the Lens of Cognitive Modeling

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

ERM Technical Session 5: Assessment

Tagged Division

Educational Research and Methods

Page Count

11

DOI

10.18260/1-2--33477

Permanent URL

https://peer.asee.org/33477

Download Count

67

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Paper Authors

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Nathan M. Hicks Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0003-2512-8484

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Nathan M. Hicks is a Ph.D. student in Engineering Education at Purdue University. He received his B.S. and M.S. degrees in Materials Science and Engineering at the University of Florida and taught high school math and science for three years.

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Kerrie A. Douglas Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-2693-5272

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Dr. Douglas is an Assistant Professor in the Purdue School of Engineering Education. Her research is focused on improving methods of assessment in large learning environments to foster high-quality learning opportunities. Additionally, she studies techniques to validate findings from machine-generated educational data.

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Heidi A. Diefes-Dux University of Nebraska, Lincoln Orcid 16x16 orcid.org/0000-0003-3635-1825

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Heidi A. Diefes-Dux is a Professor in Biological Systems Engineering at the University of Nebraska - Lincoln. She received her B.S. and M.S. in Food Science from Cornell University and her Ph.D. in Food Process Engineering from the Department of Agricultural and Biological Engineering at Purdue University. She was an inaugural faculty member of the School of Engineering Education at Purdue University. Her research focuses on the development, implementation, and assessment of modeling and design activities with authentic engineering contexts. She also focuses on the implementation of learning objective-based grading and teaching assistant training.

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Abstract

This Work-in-Progress paper presents the development of a grading process model for open-ended engineering problems with the assistance of rubrics across many graders. As many problems in engineering are open-ended, best practices dictate the use of authentic open-ended performance tasks for assessment in engineering courses. Unfortunately, evaluation of open-ended work inherently requires at least some degree of subjective judgment. However, for assessment to be considered valid, it is imperative that evaluation is fair, which includes holding all students to the same, transparently communicated expectations and standards. In large, multi-section courses, which are common in first-year engineering programs, achieving this fairness of assessment evaluation across all students becomes a significant challenge. Recognizing that the grading process consists of humans interacting with objects and making decisions, this study treats the grading process as a complex socio-technical system and describes the process of developing a model using the Functional Resonance Analysis Method (FRAM). The paper describes how the FRAM will be applied to identify sources that contribute to variability of system performance and possible corresponding control mechanisms.

Hicks, N. M., & Douglas, K. A., & Diefes-Dux, H. A. (2019, June), Understanding Grader Reliability through the Lens of Cognitive Modeling Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--33477

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