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Recommended Pedagogies to Achieve Fundamental Theorem Learning and Software Integration in Statistical Data Analysis Course (Work in Progress)

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2022 ASEE Annual Conference & Exposition


Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Industrial Engineering Division Technical Session 2

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Sanaz Motamedi University of Florida

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Mckenzie Landrum University of Florida

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Tara Ippolito University of Florida

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Austin Hayes

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This research aimed to explore different pedagogical methods for integrating software into engineering courses. This is a follow-up study to a preliminary study that was conducted during the previous semester on a quality control course. The preliminary study implemented two pedagogical methods: a traditional Instructor-Guided method and an active-learning Think-Pair-Share method. The study resulted in no statistically significant differences between the two methods. Therefore, for this study, we modified the traditional method, identified as Modified Instructor-Guided, and added two new active-learning methods, Flipped Classroom and Problem-Based Learning, in place of Think-Pair-Share. This study was conducted on an application focused statistics course, ESI3215C: Data Analysis for Industrial Applications. This is the first statistics course that students are required to take in the Industrial and Systems Engineering (ISE) program at the University of Florida. This course focuses on analysis of data encountered in ISE applications including system reliability, demand forecasting and inventory control, simulation, and quality control. Computational tools, specifically RStudio, are implemented to carry out various data analysis techniques. The first, modified pedagogical method is the Modified Instructor-Guided method in which the instructor conducts a cycle of mini-lectures and in-class exercises. The second pedagogical method is the Flipped Classroom method in which students watch short lecture videos before class and work in groups on in-class exercises. The third method is the Problem-Based Learning method in which the instructor presents a case study to be completed in groups to reach a solution, then conducts a mini-lecture at the conclusion of the case study. For each method, self-efficacy surveys and computational assessments were given to analyze student performance and comprehension. The results from this study indicate that Flipped Classroom and Modified Instructor-Guided outperform Problem-Based Learning in terms of computational understanding. However, Problem-Based Learning tends to result in higher RStudio self-efficacy, while Flipped Classroom tends to result in higher Theoretical and Statistical self-efficacy.

Motamedi, S., & Landrum, M., & Ippolito, T., & Hayes, A. (2022, August), Recommended Pedagogies to Achieve Fundamental Theorem Learning and Software Integration in Statistical Data Analysis Course (Work in Progress) Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41381

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