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Fundamental Theorem Learning With Optimum Pedagogy for Technology Integration in Quality Control Course

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

Industrial Engineering Division Technical Session 2

Tagged Division

Industrial Engineering

Page Count

17

Permanent URL

https://peer.asee.org/37212

Download Count

36

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Paper Authors

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McKenzie Landrum University of Florida

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Ms. McKenzie Landrum is an Instructional Assistant Professor in the Department of Industrial and Systems Engineering at the University of Florida. Both her Bachelors in ISE and her Masters in Management come from UF and she is currently pursuing her PhD in ISE from UF. As an Instructional Assistant Professor, Ms. Landrum has taught Spreadsheet-Based Decision Support Systems, Industrial Quality Control, Lean Production Systems, Introduction to Data Analytics, and Computer Programming for Engineers. Her research interests lie in the development of active learning methods.

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Austin Hayes University of Florida

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Mr. Austin Hayes is an undergraduate student at the University of Florida currently pursuing his B.S. in Industrial and Systems Engineering.

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Josefina Giamichelle

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Sima Sabahi University of Florida

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Sima Sabahi is an Instructional Assistant Professor in the Department of Industrial and Systems Engineering at the University of Florida.

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Sanaz Motamedi University of Florida Orcid 16x16 orcid.org/0000-0002-5389-1017

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Sanaz Motamedi is faculty member at Industrial and System Engineering, the University of Florida after her postdoctoral fellowship at the California Partners for Advanced Transportation Technology program, University of California, Berkeley. Her research interests are human factors, user experience, engineering education, and transportation safety.

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Abstract

As technology advances and new software is developed, the education system is being challenged to adapt pedagogical approaches for the smooth integration of such tools into the curriculum. These tools can be beneficial for teaching because they allow students to visualize difficult concepts and can be used to execute functions that would otherwise be time prohibitive. However, there is a concerning trend of students depending too heavily on this technology. Technology provides an avenue through which students can feign comprehension and continue advancing in the curriculum. The purpose of this study is to look at different pedagogical approaches and their effects on student’s self-efficacy and topic comprehension. To address this, we worked with a required course (ESI4221C: Industrial Quality Control) in the Industrial and Systems Engineering (ISE) curriculum at the University of Florida (UF). This course focuses on quality control and builds on statistical fundamentals while also introducing new theoretical concepts such as tests statistics, confidence intervals, p values, and ANOVA. Not only does this course teach the fundamental concepts, but it includes teaching common software that is used in industry or higher education, specifically RStudio. Different pedagogical approaches were used to teach fundamental concepts along with software to students in this study and each approach was randomly assigned to a single module. The first pedagogical approach is the Instructor Guided Method in which the instructor taught RStudio to students after each topic. The second pedagogical approach is the Think-Pair-Share Method in which students were assigned mandatory readings and instructor dedicated class time for peer-to-peer discussion. Self-efficacy surveys and conceptual/computational assessments were given for each method to determine how students felt while learning the material and their comprehension. Our preliminary data indicates that there is no statistically significant difference between the teaching methods implemented so far. The data did not reveal a difference between the methods used so far, but additional studies need to be done to assess if this result is due to low sample size or differences in difficulty between the modules that each method was assigned to. This research will be continued and expanded on in the fall 2021 semester to collect more data from additional courses and the methods applied to different modules.

Landrum, M., & Hayes, A., & Giamichelle, J., & Sabahi, S., & Motamedi, S. (2021, July), Fundamental Theorem Learning With Optimum Pedagogy for Technology Integration in Quality Control Course Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37212

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