2024 ASEE Annual Conference & Exposition

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).