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Course Outcome Assessment: Is Using the Average Good Enough?

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Innovative Approaches to Improving Student Learning

Tagged Division

Environmental Engineering

Page Count

27

DOI

10.18260/1-2--34340

Permanent URL

https://www.jee.org/34340

Download Count

18

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Paper Authors

biography

Phil Dacunto U.S. Military Academy

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COL Phil Dacunto is an Associate Professor of Environmental Engineering at the United States Military Academy at West Point, NY. He earned a Ph.D. in the field of environmental engineering at Stanford University in 2013.

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biography

Andrew Joseph Ng U.S. Military Academy

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Andrew Ng is a Captain in the United States Army and an Instructor in the Department of Geography and Environmental Engineering at the United States Military Academy. He is a 2010 graduate of the United States Military Academy with a B.S. in Environmental Engineering with honors and a 2019 graduate from the University of California, Berkeley with an M.S. in Civil and Environmental Engineering. He teaches Environmental Engineering for Community Development, Environmental Engineering Technologies, and Environmental Biological Systems.

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Abstract

Assessment of environmental engineering course outcomes is critical for measuring student achievement, evaluating course design, and ultimately assessing programs for ABET accreditation. Often such assessment relies upon the interpretation of results from direct, embedded indicators (graded events such as homework assignments or exams). The simplest approach uses the arithmetic mean of a particular graded event (or set of graded events) to assess the outcome: for example, an average of greater than 80% indicates successful achievement of the outcome. However, such an approach assumes a reasonably normal distribution of student grades, and thus may not be adequately descriptive in some cases. For example, one case where the use of the mean fails is a bimodal distribution of results: if half the students score 100% and half score 60% on a graded event, the resulting mean, 80% (which is generally considered successful) fails to capture the fact that half the students failed. Stoker, Blair, and Sobiesk (2014) proposed a “binning” approach, which accounts for the proportions of students who achieve different levels of success on each assignment; for example, if greater than 5% fail, we assess the outcome to be not successfully achieved, regardless of the overall average. Such an approach, however, is more complicated and time-intensive for faculty to employ. This study investigates the impact of course size (as measured by number of students) on assessment results. Specifically, we present a comparison of course outcome assessment results using both the simpler “mean” and more complicated “binning” approaches for three large (more than 160 students) and two small (less than 35 students) environmental engineering and science courses. The results indicate that the small courses had a greater drop in outcome achievement than the large ones when using the “binning” vs the “mean” approach; however, the "binning" approach could still be useful in these courses with minor modification. In addition, we present some adjustments to the method proposed by Stoker et al. (2014) to make overall outcome assessment more efficient. This study will inform programs as to the conditions under which the more time-intensive “binning” approach is worthwhile, and will enable them to implement the approach more effectively if they so choose.

Ref: Stoker, Geoff; Blair, Jean; and Edward Sobiesk. “Meaningful Assessment.” Proceedings of the 15th Annual Conference on Information Technology Education. Atlanta, GA: 15-18 October, 2014.

Dacunto, P., & Ng, A. J. (2020, June), Course Outcome Assessment: Is Using the Average Good Enough? Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34340

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