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Teacher Impact on Student Learning Using LC-DLM Implementations in the Classroom

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Conference

2022 ASEE Annual Conference & Exposition

Location

Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

Chemical Engineering Division Poster Session

Page Count

11

Permanent URL

https://peer.asee.org/41713

Download Count

26

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Paper Authors

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Heidi Curtis Campbell University

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Jacqueline Gartner Campbell University

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Jacqueline is an Assistant Professor and founding faculty at Campbell University School of Engineering. As part of her role, she teaches many of the chemical engineering courses for students in the middle years.

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Prashanta Dutta Washington State University

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Olusola Adesope Washington State University

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Bernard Van Wie Washington State University

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Carah Watson

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Abstract

Our team has developed Low-Cost Desktop Learning Modules (LCDLMS) as tools to study transport phenomena aimed at providing hands-on learning experiences. With an implementation design embedded in the community of inquiry framework, we disseminate units to professors across the country and train them on how to facilitate teacher presence in the classroom with the LC-DLMs. Professors are briefed on how create a homogenous learning environment for students based on best-practices using the LC-DLMs. By collecting student cognitive gain data using pre/posttests before and after students encounter the LC-DLMs, we aim to isolate the variable of the professor on the implementation with LC-DLMs. Because of the onset of COVID-19, we have modalities for both hands-on and virtual implementation data. An ANOVA whereby modality was grouped and professor effect was the independent variable had significance on the score difference in pre/posttest scores (p<0.0001) and on posttest score only (p=0.0004). When we divide out modality between hands-on and virtual, an ANOVA with an F-test using modality as the independent variable and professor effect as the nesting variable also show significance on the score difference between pre and posttests (p-value=0.0236 for hands-on, and p-value=0.0004 for virtual) and on the posttest score only (p-value=0.0314 for hands-on, and p-value<0.0001 for virtual). These results indicate that in all modalities professor had an effect on student cognitive gains with respect to differences in pre/posttest score and posttest score only. Future will focus on qualitative analysis of features of classrooms yield high cognitive gains in undergraduate engineering students.

Curtis, H., & Gartner, J., & Dutta, P., & Adesope, O., & Van Wie, B., & Watson, C. (2022, August), Teacher Impact on Student Learning Using LC-DLM Implementations in the Classroom Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. https://peer.asee.org/41713

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