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Does Curricular Complexity Imply Program Quality?

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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 19: Thinking about the Engineering Curriculum

Tagged Division

Educational Research and Methods

Page Count

13

Permanent URL

https://peer.asee.org/32677

Download Count

7

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Paper Authors

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Greg L. Heileman University of Kentucky Orcid 16x16 orcid.org/0000-0002-5221-5682

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Gregory L. Heileman received the BA degree from Wake Forest University in 1982, the MS degree in Biomedical Engineering and Mathematics from the University of North Carolina-Chapel Hill in 1986, and the PhD degree in Computer Engineering from the University of Central Florida in 1989. In 1990 he joined the Department of Electrical and Computer Engineering at the University of New Mexico, Albuquerque, NM, where he is currently a Professor. Since 2011 he has served as the Associate Provost for Curriculum at the University of New Mexico. During 1998 he held a research fellowship at the Universidad Carlos III de Madrid, and in 2005 he held a similar position at the Universidad Politénica de Madrid. His research interests are in information security, the theory of computing and information, machine learning, and data structures and algorithmic analysis. He is the author of the text Data Structures, Algorithms and Object-Oriented Programming, published by McGraw-Hill in 1996.

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William G. Thompson-Arjona University of Kentucky

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Will Thompson is a graduate student in the department of electrical and computer engineering at the University of Kentucky. Prior to this, he was a hardware development engineer in the industrial automation sector working for Rockwell Automation (NYSE: ROK). He earned a B.S. in bioelectrical engineering from Marquette University in 2015. His interests include optimization, embedded hardware systems, signal processing, and machine learning.

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Orhan Abar University of Kentucky

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Orhan Abar is a Ph.D. student in the Computer Science (CS) Department at the University of Kentucky. He graduated with an M.S in CS from the University of Texas at San Antonio in 2013. He obtained a B.S. in Computer Engineering from Firat University in 2009. His research interests include Deep Learning, Data Mining, Frequent Pattern Mining, and Optimization.

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Hayden W. Free University of Kentucky

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Hayden Free is an undergraduate student studying Computer Science at the University of Kentucky. His focused area of interests include distributed systems, cloud architecture, and software design.

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Abstract

A number of metrics exist for quantifying the complexity of academic program curricula. Complexity in this case relates the extent to which the structure of a curriculum impacts a student's ability to progress through that curriculum towards graduation. The ability to quantify curricular complexity in this manner allows us to order programs according to their complexity, and to compare and contrast similar programs at different institutions according to their complexity measures. When sharing this type of information with faculty and program administrators, those at programs at the higher end of the complexity scale often speculate that high complexity implies a higher quality program. Which leads to the more general question, what does curricular complexity tell us about program quality? In cursory investigations of this conjecture, a surprising relationship emerged. Specifically, anecdotal review provided significant evidence to support the proposition that higher quality engineering programs have lower complexity curricula. It is worth noting that if this proposition is indeed true, then the contrapositive proposition is also true, namely that higher complexity curricula imply lower quality programs. In this study we collected sufficient data to determine the veracity of this proposition for undergraduate electrical engineering programs. Additional work is underway to validate this proposition for other engineering areas.

The methodology employed in this study involved partitioning a large set of undergraduate electrical engineering curricula into three categories (high, medium and low) according to their quality. The curricular complexity variance within and between these groups was then analyzed using ANOVA methodologies. Because program quality is a subjective measure, we used the 2018 U.S. News & World Report undergraduate program rankings as a proxy for quality. This ranking orders programs from 1-205, with formal ranking designation given to programs in the 1-177 range. The first group included schools in the top decile of the ranking, the medium group included schools from the fourth and fifth deciles, and the low group included those schools that were grouped together at the bottom of the list. The null hypothesis was that there are significant differences between the intragroup and intergroup curricular complexity measures. Our analysis found that with a low margin of error, and a 95% confidence interval, the null hypothesis should be accepted. Furthermore, the most significant difference was between the set of highly-ranked programs and the medium-ranked programs, with a less pronounced difference between the medium- and low-ranked programs.

The principle of Occam's razor is often applied to guide engineering designs towards the simplest and therefore best solutions. One of the most popular versions of this principle states, "Entities are not to be multiplied without necessity." We posit that this study indicates this principle applies to curricula. Namely, the simplest curriculum (in terms of complexity) that allows students to attain a program's learning outcomes yields the best student success outcomes and therefore the highest quality program.

Heileman, G. L., & Thompson-Arjona, W. G., & Abar, O., & Free, H. W. (2019, June), Does Curricular Complexity Imply Program Quality? Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/32677

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