Investigating Computational Identity: A Qualitative Study of Undergraduates Participating in a Thermodynamics Course

Huma Shoaib, Purdue University

Abstract

Biological engineering includes a higher representation of women (Yoder, 2015) than most science, technology, engineering, mathematics and information technology fields, while computer science has seen a precipitous decline in women’s representation. However, disciplines like biological engineering are becoming more computationally intensive (Savage, 2018), incorporating computational skills like computer programming. As biological engineering is adapting tools and practices from computer science, there is potential that the factors that have contributed to the decline in the participation of women in computer science may impact women’s participation in biological engineering as well. In engineering education research, discipline-based identity research is one framework that is used to understand retention and persistence issues because students' sense of self as an engineering and belonging is a critical factor when it comes to deciding to stay or leave discipline.The current study adopts the framing of identity, which comes from social identity theory (Stryker & Burke, 2000) and symbolic interactionism (Burke & Stets, 2009). Recently, some work has emerged on understanding the computing identity of computer science and engineering students (Garcia, Hazari, Weiss, & Solis, 2019) and the computational identity of K-12 mathematics students (Kong & Wang, 2020). However, a clear gap exists in research that explores the computational identities of undergraduate engineering students. This dissertation utilizes an established theoretical framework from computer science education research for understanding computing identity (Mahadeo et al., 2020). For the current study, the operationalization of computational identity for research in engineering could provide a transition from one discipline (computing) to another (engineering) because of a similar skillset (programming) while also contributing to the engineering education body of knowledge.For this qualitative investigation, 23 semi-structured interviews were conducted with undergraduates who were enrolled in a computationally intensive thermodynamics course. During the analysis phase, coding was inductive as well as deductive in nature; in vivo coding used participants' words from participant's sentences as codes while the interviews were also coded for constructs and sub-constructs based on the theoretical framework. Axial coding helped to combine related codes that emerged from the inductive and deductive coding into themes, and the themes addressed each of the research questions.The findings present an emergent thematic definition of a “computational person” constructed from students’ perceptions and experiences. A detailed description of a computational person based on student perspective came out as someone who is proficient with mathematics, logical thinking, and computer programming and can make rational decisions and encompass multiple perspectives towards problem-solving and solution representation. Participants also talked about their own computational identity in relationship to how they defined a computational person. Most of the participants described that their computational identity as “in the making.”This study's second contribution is the investigation of congruence between the participants' computational identity and other identities they held: gender, engineering, and artistic. The qualitative analysis revealed that for some students there are incongruences between an artistic/creative identity and a computational identity and incongruences between being female and having a computational identity. In contrast, engineering identity and computational identity were found to be congruent. The interview participants also noted ways that pedagogical approaches used in the thermodynamics course supported the development of computational confidence. This study's findings support computational practice and cooperative learning-based instruction for computational identity development.

Degree

Ph.D.

Advisors

Cardella, Purdue University.

Subject Area

Bioengineering|Computer science|Computer Engineering|Higher education|Mathematics|Physics|Computational physics|Education|Educational administration|Finance|Gender studies|Thermodynamics

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