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Using Topological Data Analysis in Social Science Research: Unpacking Decisions and Opportunities for a New Method

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

ERM Technical Session 1: Methods Refresh: Approaches to Data Analysis in Engineering Education Research

Tagged Division

Educational Research and Methods

Tagged Topic

Diversity

Page Count

15

Permanent URL

https://peer.asee.org/33522

Download Count

1

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Paper Authors

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Allison Godwin Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0002-0741-3356

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Allison Godwin, Ph.D. is an Assistant Professor of Engineering Education at Purdue University. Her research focuses what factors influence diverse students to choose engineering and stay in engineering through their careers and how different experiences within the practice and culture of engineering foster or hinder belongingness and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning, to understand engineering students’ identity development. She has won several awards for her research including the 2016 American Society of Engineering Education Educational Research and Methods Division Best Paper Award and the 2018 Benjamin J. Dasher Best Paper Award for the IEEE Frontiers in Education Conference. She has also been recognized for the synergy of research and teaching as an invited participant of the 2016 National Academy of Engineering Frontiers of Engineering Education Symposium and the Purdue University 2018 recipient of School of Engineering Education Award for Excellence in Undergraduate Teaching and the 2018 College of Engineering Exceptional Early Career Teaching Award.

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Aaron Robert Hamilton Thielmeyer Purdue University, West Lafayette

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Aaron Thielmeyer is a mechanical engineering undergraduate student at Purdue University.

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Jacqueline Ann Rohde Purdue University, West Lafayette

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Jacqueline A. Rohde is a graduate student at Purdue University as the recipient of an NSF Graduate
Research Fellowship. Her research interests in engineering education include the development student identity and
attitudes, with a specific focus on the pre-professional identities of engineering undergraduates who join non-
industry occupations upon graduation.

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Dina Verdín Purdue University, West Lafayette Orcid 16x16 orcid.org/https://0000-0002-6048-1104

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Dina Verdín is a Ph.D. Candidate in Engineering Education and M.S. student in Industrial Engineering at Purdue University. She completed her B.S. in Industrial and Systems Engineering at San José State University. Dina is a 2016 recipient of the National Science Foundation’s Graduate Research Fellowship and an Honorable Mention for the Ford Foundation Fellowship Program. Her research interest focuses on changing the deficit base perspective of first-generation college students by providing asset-based approaches to understanding this population. Dina is interested in understanding how first-generation college students author their identities as engineers and negotiate their multiple identities in the current culture of engineering.

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Brianna Shani Benedict Purdue University, West Lafayette

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Brianna Benedict is a Graduate Research Assistant in the School of Engineering Education at Purdue University. She completed her Bachelor's and Master's of Science in Industrial and Systems Engineering at North Carolina Agricultural & Technical State University. Her research interest focuses on interdisciplinary students' identity development, belongingness in engineering, and recognition.

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Rachel Ann Baker

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Jacqueline Doyle Harvard-Smithsonian Center for Astrophysics

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Jacqueline Doyle is a Postdoctoral Fellow at the Harvard-Smithsonian Center for Astrophysics. Her current research interests include professional development for K-12 science teachers; factors influencing student career interests; diversity, inclusion, and equity in STEM; and student identity development. She graduated from Florida International University with a Ph.D. in Physics.

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Abstract

This research paper describes a new statistical method for engineering education, Topological Data Analysis (TDA), and considers the important decisions made during analysis and their impact on the quality of the results. We also describe why this new method may provide novel ways of understanding multidimensional data for student attitudes, beliefs, and mindsets.

TDA is a statistical method that can map structure within highly dimensional, noisy, and incomplete data. It is also insensitive to the particular distance function chosen to detect the persistent structure or typology in the data. In some ways, TDA is like a robust cluster analysis. However, unlike cluster analysis, which attempts to break datasets into distinct (or probabilistic) groups, TDA allows for data with progressions rather than clear distinctions. Rather than being focused on breaking data into defined groups, TDA maps the connections among data and provides additional details within the data structure that cannot be captured using cluster analysis. Since its development in 2009, TDA has been used in a number of different fields including medicine, business, and sports. However, few studies have used this technique with social science data. We believe that this technique can be particularly useful to engineering education researchers who deal with complex data that is often multidimensional, noisy, and incomplete.

In this paper, we discuss the considerations that researchers must understand in conducting TDA with engineering education data. In analysis, a researcher must choose a filtering method, number of nearest neighbors (k), number of filter slices (n), overlap in data, and cut height (ε) for each dimension. The importance and effect on the consistency and quality of the data differs for each decision. Some have a large impact on the results of the analysis [e.g., cut height (ε)], while others have a moderate impact on the resulting map appearance but not key structural features identified [e.g., number of filter slices (n)].

We illustrate these methodological decisions as well as the results of TDA and its usefulness for engineering education using data from a project investigating first-year engineering students’ underling attitudes, beliefs, and mindsets to characterize the latent diversity of these students. A paper-and-pencil survey was administered to 3,855 students at 32 ABET accredited institutions across the U.S. in fall 2017. After cleaning the data using attention checks within the survey, a total of 3,711 student responses were examined for validity evidence. Exploratory factor analysis (for newly developed scales) and confirmatory factor analysis (for existing scales) was conducted. The resulting factors with strong validity evidence and high variability among engineering students were used in the TDA to map students’ latent diversity. The results of this map indicate six distinct data progressions as well as a sparse group of students whose responses were not similar to the majority of the dataset. This work illustrates the opportunities for using TDA and provides a discussion of the different researcher decisions that are involved in this statistical technique.

Godwin, A., & Thielmeyer, A. R. H., & Rohde, J. A., & Verdín, D., & Benedict, B. S., & Baker, R. A., & Doyle, J. (2019, June), Using Topological Data Analysis in Social Science Research: Unpacking Decisions and Opportunities for a New Method Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/33522

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