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Modeling Student Performance in an Introductory Chemical Engineering Course

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

ChemE Curriculum: Freshman and Sophomore

Tagged Division

Chemical Engineering

Page Count

9

Permanent URL

https://peer.asee.org/30817

Download Count

13

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Paper Authors

biography

Kyle Joe Branch University of Utah

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Kyle Branch is a fifth-year graduate student at the University of Utah Department of Chemical Engineering. He has helped develop and teach two freshman courses, using the materials and methods described in this paper. His main research interest is in engineering education, focusing on the creation and analysis of interactive simulations for undergraduate chemical engineering courses.

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biography

Anthony Butterfield University of Utah

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Anthony Butterfield is an Assistant Professor (Lecturing) in the Chemical Engineering Department of the University of Utah. He received his B. S. and Ph. D. from the University of Utah and a M. S. from the University of California, San Diego. His teaching responsibilities include the senior unit operations laboratory and freshman design laboratory. His research interests focus on undergraduate education, targeted drug delivery, photobioreactor design, and instrumentation.

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Abstract

We have developed an open-ended, laboratory-based introduction to chemical engineering, a course coupling traditional, hands-on, and virtual learning components. This course is supported by an online homework system which we developed to administer and track student usage on a variety of course materials, including homework assignments, lab safety training quizzes, course surveys, and simulated laboratory experiments. This system is also used to track student peer evaluations and which equipment students are certified to use in the lab.

Using the system’s tracking capabilities, we have gathered a sizable collection of data on various student characteristics and behaviors in the course, and we have performed regression analyses to search for insights into how these characteristics and behaviors may relate to student performance, as measured by their overall score in the course. From these analyses, two variables emerged as highly predictive of student performance: scores on peer evaluations and homework submission timeliness. This relationship remains strong even when the measure of student performance is adjusted so that student peer evaluations and late penalties on homework assignments do not directly factor into their adjusted overall score. We discuss potential explanations for and practical implications of this result.

Branch, K. J., & Butterfield, A. (2018, June), Modeling Student Performance in an Introductory Chemical Engineering Course Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30817

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