Investigating students’ perceptions and experiences to broaden participation in computing

Ojha, Vidushi

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https://hdl.handle.net/2142/124316 Copy

Description

Title

Investigating students’ perceptions and experiences to broaden participation in computing

Author(s)

Ojha, Vidushi

Issue Date

2024-04-18

Director of Research (if dissertation) or Advisor (if thesis)

Lewis, Colleen M

Doctoral Committee Chair(s)

Lewis, Colleen M

Committee Member(s)

• Zilles, Craig
• Cunningham, Kathryn
• Hovey, Christopher L

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• broadening participation in computing
• diversity
• equity
• inclusion
• computer science
• self-efficacy
• sense of belonging
• historically underrepresented groups in computing

Abstract

My work seeks to investigate students’ perceptions of and experiences in higher education computer science (CS) courses in order to inform efforts to broaden participation in computing (BPC). In the U.S., people who identify as women, Black/African American, Hispanic, Latina/o/x/*, Native American, Native Alaskan, Native Hawaiian, Pacific Islanders, and/or disabled are considered historically underrepresented in computing. I refer to these groups as historically underrepresented groups, or HUGs. Individuals from HUGs are not proportionally represented in the CS educational system or in the computing workforce; BPC efforts aim to recruit and retain students from HUGs in order to address this inequity. Prior work has identified many factors that may affect students’ recruitment and retention in CS. Of particular interest to this dissertation are the influences of students’ prior perceptions of computing and of their experiences once they are in the field. This dissertation consists of four studies investigating aspects of students’ perceptions and experiences in computing and how they relate to important outcomes for recruitment and retention in the field. The first study investigated students’ perceptions of specializations within the field of CS, specifically artificial intelligence (AI) and cybersecurity, before they have personal experience in these specializations. This qualitative study examined students’ expressed beliefs about AI and cybersecurity that may affect their interest in pursuing these fields. Understanding these perceptions may inform efforts to bolster students’ interest in AI and cybersecurity towards the goal of increasing participation of students from HUGs in these specializations. The second study in this dissertation addressed the experiences students from HUGs may have regarding understanding the expectations of a doctoral CS program. Through interviews with PhD students, we found that students' initial expectations of graduate school were often incomplete or inaccurate, and that policies such as formal mentorship systems may positively impact PhD students’ experiences and ability to succeed, thereby increasing their retention in CS. The third study examined undergraduate students’ computing self-efficacy, i.e., students’ beliefs that they can achieve desired outcomes in their computing courses. We found that identifying as Asian, Black, Native, Hispanic, non-binary, and/or a woman were statistically significantly associated with lower computing self-efficacy, even when controlling for prior CS experience. This work further used an intersectional approach to show that identifying as Asian and non-binary correlates with lower computing self-efficacy. The fourth study investigated whether self-efficacy and sense of belonging correlate with instructional transparency, which aims to make course’s learning goals, evaluation criteria, and path to success clear and accessible to students. Our findings show group differences in students’ perception of transparency, as students who identify as women, first-generation college students, and/or disabled reported perceiving less transparency. We also demonstrate that perception of instructional transparency has a positive correlation with students’ self-efficacy and sense of belonging in computing while controlling for important confounding variables, such as prior CS experience. As a whole, my work aims to investigate students’ perceptions of and experiences in CS education in order to inform and support BPC efforts. Collectively, this research provides insights into the kinds of institution-level policies and classroom-level practices that may contribute to diversity, inclusion, and equity efforts. Findings from each of the studies suggest particular policies and practices, such as a formal mentorship system for PhD students and using transparent teaching in CS classrooms, as well as avenues for future research that may shed further light on how to recruit and retain students from HUGs in computing.

Graduation Semester

2024-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2024 Vidushi Ojha

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