WIP: Barriers to Developing Computing Identity in Hispanic-serving Community College Introductory Artificial Intelligence Courses

Sarah L. Rodriguez; Paul Charles Bigby; Antarjot Kaur

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: July 12, 2024
Conference Session: Two-Year College Potpourri
Tagged Division: Two-Year College Division (TYCD)
Tagged Topic: Diversity
Permanent URL: https://peer.asee.org/48289

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Paper Authors

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Sarah L. Rodriguez Virginia Polytechnic Institute and State University orcid.org/0000-0002-3409-7096

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Sarah L. Rodriguez is an Associate Professor of Engineering Education and an affiliate faculty member with the Higher Education Program at Virginia Tech. Her engineering education research agenda centers upon engineering and computing identity development of historically marginalized populations at higher education institutions. Currently, Dr. Rodriguez is involved with several large-scale interdisciplinary research projects focused on institutional environments and STEM identity development are sponsored by the National Science Foundation (NSF) and the Kapor Center. In recent years, she was selected as an Early Career Awardee and Faculty Fellow with the American Association of Hispanics in Higher Education (AAHHE) and a NASPA Emerging Faculty Leader. She also received the Barbara Townsend Early Career Scholar Award by the Council for the Study of Community Colleges (CSCC) and gave the distinguished ASHE-CAHEP Barbara Townsend Lecture. To learn more about her current projects, visit http://sarahlrodriguez.com/

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Paul Charles Bigby Virginia Polytechnic Institute and State University

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Paul C. Bigby is a graduate student at Virginia Polytechnic Institute and State University pursuing a Ph.D. in Engineering Education, where he serves as a graduate research assistant. Paul also earned his Bachelor’s from Virginia Tech in Mechanical Engineering, and Master's degrees from both the University of Michigan and Indiana University. His research interests are in broadening participation in engineering with a systemic or institutional focus.

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Antarjot Kaur Virginia Polytechnic Institute and State University

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Antarjot Kaur is currently a Ph.D. student situated in the Engineering Education Department at Virginia Polytechnic Institute and State University. Antarjot has received her B.S. and M.S. in Bioengineering from George Mason University with specialization in Medical Imaging and Devices. Her research interests include broadening participation in engineering, engineering pedagogy, and developing career pathways for Bioengineering/Biomedical Engineering students.

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Abstract

Computing identity has known to be a predictor of selecting and persisting in computing education. Given the recent emphasis on increasing representation of minoritized groups in computing programs, it is important to facilitate the development of their computing identities within a disciplinary culture that may be exclusionary. In many ways, higher education institutions – even HSCCs – can put barriers in place that make building a computing identity difficult. As such, introductory computing courses at community colleges are a key entry point for minoritized students and should therefore work towards removing every possible barrier to students developing their computing identities. The purpose of this study is to identify practices at the HSCC that are inhibiting or detracting from the development of computing identity. The study focuses on two main research questions: 1) What practices occur in introductory computing courses that inhibit the development of computing identity for students at an HSCC? 2) What are the unique social identities or circumstances of students at an HSCC that make these practices detrimental to developing a computing identity? We will examine the experiences of students through the lens of computing identity as conceptualized by Lunn et al. (2022). The computing identity framework consists of four key components: (1) interest, (2) sense of belonging, (3) performance/competence, and (4) recognition. The model also examines the interaction of the student’s social identities (e.g., gender, race, ethnicity, socio-economic status) with academic computing experiences and how they work together to influence computing identity. As part of an ongoing research study, the authors conducted semi-structured phenomenological interviews with 19 students enrolled in introductory AI courses at an HSCC. Interviews (~60 minutes in length) were recorded, transcribed, and coded to identify common essences from the participants. To address trustworthiness, authors explored their positionalities and how their backgrounds and identities influenced the research design, data collection, and interpretation of the data. We identified several key practices that occurred in the introductory AI computing courses that inhibited the development of computing identity for students at an HSCC. Delivery method and computing requirements (e.g., online format, hidden course times, hardware) created accessibility issues for students, particularly those with chronic health or financial issues. There were also barriers associated with pre-requisite computing knowledge (e.g., Python coding). Together, these barriers impacted competence and performance beliefs of students which, at times, made it difficult to build and maintain a computing identity. We also uncovered issues with sense of belonging and recognition for some students (e.g., online format difficult to build peer recognition; women not seeing themselves reflected in computing). This finding illustrates the interplay of academic experiences and social identities in developing computing identity. We found that the introductory AI courses presented barriers to computing identity development to HSCC students from certain social identities or circumstances (e.g., being post-traditional, first generation, working, or low income).

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Format

Rodriguez, S. L., & Bigby, P. C., & Kaur, A. (2024, June), WIP: Barriers to Developing Computing Identity in Hispanic-serving Community College Introductory Artificial Intelligence Courses Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/48289

TY - CPAPER
AB - Computing identity has known to be a predictor of selecting and persisting in computing education. Given the recent emphasis on increasing representation of minoritized groups in computing programs, it is important to facilitate the development of their computing identities within a disciplinary culture that may be exclusionary. In many ways, higher education institutions – even HSCCs – can put barriers in place that make building a computing identity difficult. As such, introductory computing courses at community colleges are a key entry point for minoritized students and should therefore work towards removing every possible barrier to students developing their computing identities.
The purpose of this study is to identify practices at the HSCC that are inhibiting or detracting from the development of computing identity. The study focuses on two main research questions:
1) What practices occur in introductory computing courses that inhibit the development of computing identity for students at an HSCC?
2) What are the unique social identities or circumstances of students at an HSCC that make these practices detrimental to developing a computing identity?
We will examine the experiences of students through the lens of computing identity as conceptualized by Lunn et al. (2022). The computing identity framework consists of four key components: (1) interest, (2) sense of belonging, (3) performance/competence, and (4) recognition. The model also examines the interaction of the student’s social identities (e.g., gender, race, ethnicity, socio-economic status) with academic computing experiences and how they work together to influence computing identity.
As part of an ongoing research study, the authors conducted semi-structured phenomenological interviews with 19 students enrolled in introductory AI courses at an HSCC. Interviews (~60 minutes in length) were recorded, transcribed, and coded to identify common essences from the participants. To address trustworthiness, authors explored their positionalities and how their backgrounds and identities influenced the research design, data collection, and interpretation of the data.
We identified several key practices that occurred in the introductory AI computing courses that inhibited the development of computing identity for students at an HSCC. Delivery method and computing requirements (e.g., online format, hidden course times, hardware) created accessibility issues for students, particularly those with chronic health or financial issues. There were also barriers associated with pre-requisite computing knowledge (e.g., Python coding). Together, these barriers impacted competence and performance beliefs of students which, at times, made it difficult to build and maintain a computing identity. We also uncovered issues with sense of belonging and recognition for some students (e.g., online format difficult to build peer recognition; women not seeing themselves reflected in computing). This finding illustrates the interplay of academic experiences and social identities in developing computing identity. We found that the introductory AI courses presented barriers to computing identity development to HSCC students from certain social identities or circumstances (e.g., being post-traditional, first generation, working, or low income).

AU - Sarah L. Rodriguez
AU - Paul Charles Bigby
AU - Antarjot Kaur
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - WIP: Barriers to Developing Computing Identity in Hispanic-serving Community College Introductory Artificial Intelligence Courses
UR - https://peer.asee.org/48289
ER -