Work in Progress: Analyzing a Distributed Expertise Model in an Undergraduate Engineering Course

Sara Willner-Giwerc; Kristen B Wendell

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Conference: 2019 FYEE Conference
Location: Penn State University , Pennsylvania
Publication Date: July 28, 2019
Start Date: July 28, 2019
End Date: July 30, 2019
Conference Session: M1B: WIP - Learning Experiences 2
Tagged Topic: FYEE Conference - Paper Submission
Page Count: 5
DOI: 10.18260/1-2--33738
Permanent URL: https://peer.asee.org/33738
Download Count: 394

Paper Authors

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Sara Willner-Giwerc Tufts University

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Sara Willner-Giwerc is currently pursuing a Ph.D. in mechanical engineering at Tufts University. She is a National Science Foundation Graduate Research Fellow, which supports her research at the Tufts Center for Engineering Education and Outreach (CEEO) on technological tools and pedagogical approaches for facilitating hands on learning in educational settings.

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Kristen B Wendell Tufts University

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Kristen Wendell is Assistant Professor of Mechanical Engineering and Adjunct Assistant Professor of Education at Tufts University. Her research efforts at at the Center for Engineering Education and Outreach focus on supporting discourse and design practices during K-12, teacher education, and college-level engineering learning experiences, and increasing access to engineering in the elementary school experience, especially in under-resourced schools. In 2016 she was a recipient of the U.S. Presidential Early Career Award for Scientists and Engineers (PECASE). https://engineering.tufts.edu/me/people/faculty/kristen-bethke-wendell

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Abstract

This work in progress presents an analysis of a distributed expertise approach to teaching computational thinking in a first-year undergraduate engineering course. The distributed expertise model is an instructional approach in which each student specializes in one topic area that falls within the broader content goals of the course. Students spend a portion of the course gaining expertise in their focused topic area, and then join together with one student from every area to form a mixed-expertise group for a major culminating project. This final project is designed to require knowledge developed in each of the specializations, thus leveraging and requiring the expertise distributed throughout the group. This is distinctly different from a traditional content delivery model where the goal is to teach all of the students the same content throughout the duration of the course. In a distributed expertise model, the goal is to enable all students to develop the same fundamental skills and content awareness, but allow them to specialize and gain deeper expertise in just one content application area. In other fields, distributed expertise and jigsaw learning are used to promote authentic student discussions and increase active learning. However, these techniques have not yet been widely applied to teach computational thinking. This study analyzes four sections of the Introduction to Computing in Engineering course, which is required of first-year engineering students at a research university in Massachusetts. The overall objective of the course is to teach students how to apply computational tools to engineering problems and tasks. The course was taught by four different professors. One professor used a distributed expertise model and the other three used traditional content delivery methods. In the distributed expertise course, students were broken up into four specialty groups: computer vision and image processing, sensing and actuating, data acquisition and processing, and data analytics and visualization. Students spent the beginning of the semester learning as one large group and then broke into their specialty groups for the middle portion of the semester. They then spent the remainder of the semester working in final project groups that consisted of one student from each of the specialty groups.

This mixed methods comparative case study explores the following two research questions: (1) In a course that uses a distributed expertise model, in what ways do students demonstrate knowledge and competency in computer science fundamentals, data collection methods, data analysis techniques, and data communication, and how does this compare to students taught in a traditional model? and (2) How does the complexity, solution diversity, functionality, and emotional investment in students’ final projects compare between a distributed expertise model and a traditional content delivery model? Data sources include student surveys taken before and after the course, student coursework, and in class observations. We hypothesize that students taught using the distributed expertise model will show an increase in inclusion, engagement, and mechanistic reasoning. This paper will discuss data collection methods, preliminary findings from the data, and plans for future work.

Citation
Format

Willner-Giwerc, S., & Wendell, K. B. (2019, July), Work in Progress: Analyzing a Distributed Expertise Model in an Undergraduate Engineering Course Paper presented at 2019 FYEE Conference , Penn State University , Pennsylvania. 10.18260/1-2--33738

TY - CPAPER
AB - This work in progress presents an analysis of a distributed expertise approach to teaching computational thinking in a first-year undergraduate engineering course. The distributed expertise model is an instructional approach in which each student specializes in one topic area that falls within the broader content goals of the course. Students spend a portion of the course gaining expertise in their focused topic area, and then join together with one student from every area to form a mixed-expertise group for a major culminating project. This final project is designed to require knowledge developed in each of the specializations, thus leveraging and requiring the expertise distributed throughout the group. This is distinctly different from a traditional content delivery model where the goal is to teach all of the students the same content throughout the duration of the course. In a distributed expertise model, the goal is to enable all students to develop the same fundamental skills and content awareness, but allow them to specialize and gain deeper expertise in just one content application area. In other fields, distributed expertise and jigsaw learning are used to promote authentic student discussions and increase active learning. However, these techniques have not yet been widely applied to teach computational thinking. This study analyzes four sections of the Introduction to Computing in Engineering course, which is required of first-year engineering students at a research university in Massachusetts. The overall objective of the course is to teach students how to apply computational tools to engineering problems and tasks. The course was taught by four different professors. One professor used a distributed expertise model and the other three used traditional content delivery methods. In the distributed expertise course, students were broken up into four specialty groups: computer vision and image processing, sensing and actuating, data acquisition and processing, and data analytics and visualization. Students spent the beginning of the semester learning as one large group and then broke into their specialty groups for the middle portion of the semester. They then spent the remainder of the semester working in final project groups that consisted of one student from each of the specialty groups.

This mixed methods comparative case study explores the following two research questions: (1) In a course that uses a distributed expertise model, in what ways do students demonstrate knowledge and competency in computer science fundamentals, data collection methods, data analysis techniques, and data communication, and how does this compare to students taught in a traditional model? and (2) How does the complexity, solution diversity, functionality, and emotional investment in students’ final projects compare between a distributed expertise model and a traditional content delivery model? Data sources include student surveys taken before and after the course, student coursework, and in class observations. We hypothesize that students taught using the distributed expertise model will show an increase in inclusion, engagement, and mechanistic reasoning. This paper will discuss data collection methods, preliminary findings from the data, and plans for future work.

AU - Sara Willner-Giwerc
AU - Kristen B Wendell
CY - Penn State University , Pennsylvania
DA - 2019/07/28
PB - ASEE Conferences
TI - Work in Progress: Analyzing a Distributed Expertise Model in an Undergraduate Engineering Course
UR - https://peer.asee.org/33738
DO - 10.18260/1-2--33738
ER -