Author: godwina

Authors: Allison Godwin, Aaron Robert Hamilton Thielmeyer, Jacqueline Rohde, Dina Verdín, Brianna Shani Benedict, Rachel Ann Baker & Jacqueline Doyle

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.

Read online for free at ASEE PEER: Using Topological Data Analysis in Social Science Research: Unpacking Decisions and Opportunities for a New Method

Authors: Brianna Shani Benedict, Dina Verdín, Rachel Ann Baker, Allison Godwin & Thaddeus Milton

Abstract: Curricular expectations for engineering students are steadily expanding to encompass a diverse set of competencies and skills that ensure students are prepared to address the global challenges of engineering. This expansion highlights a need for educators to not only rethink how they educate the next generation of engineers, but also a need to cultivate “diversity of thought” within the culture of engineering. Earlier studies about diversity have focused on understanding how to increase the number of underrepresented students (i.e., women, students of color, and first-generation college students) who persist in STEM fields. However, there is a shift in how we (i.e., society, industry, and academia) define what it means to be diverse. In this paper, we examined how 12 diverse first-year engineering students described how their peers enact different ways of thinking and being in engineering, as well as how those differences influence whether their peers are perceived as someone who belongs in engineering. The participants acknowledged the cultural and gender differences among their peers; however, they primarily described how their peers were different based on their skill-set (i.e., technical, creative, and interpersonal), ways of thinking, and interests. These findings begin to help us understand how students define normative attitudes in engineering and the perception of what it means to be an engineer.

Read online for free at ASEE PEER: Uncovering Latent Diversity: Steps Towards Understanding ‘What Counts’ and ‘Who Belongs’ in Engineering Culture

Authors: Allison Godwin

Abstract: This theory paper explores how diversity apart from social identities like race and gender is framed in the engineering education literature and how these concepts promote a different but compatible approach to understanding diversity—latent diversity. Latent diversity is a new approach to diversity work that captures underlying affective and cognitive differences that provide potential sources for innovation but are not visible. This approach does not examine other non-visible social identities like sexual orientation, first-generation status, socioeconomic status, etc. Prior literature suggests that diversity in approaches, problem solving, and ways of thinking improve innovation in engineering design more reliably than does diversity along the lines of age, race, gender, etc. However, the process of enculturating students into engineering through engineering curriculum often creates homogeneity in students’ approaches to problems, ways of thinking, and attitudes. In this paper, I explore a limited set of existing research on diversity from these underlying perspectives including identities, alternative ways of thinking and being, motivation, cognitive diversity, and innovation and creativity. This work synthesizes the findings of these studies to paint a rich picture of how students develop different attitudes and skills to navigate their paths within engineering. Additionally, this work provides an evidence-based argument for the importance of recognizing and understanding latent diversity to promote a more inclusive environment in engineering and recruit, educate, retain, and graduate more innovative and diverse engineers. This paper opens the conversation about a new, but complementary, focus for developing a STEM workforce rich in talent and capable of adapting to the changing STEM landscape.

Read online for free at ASEE PEER: Unpacking Latent Diversity