Does a Taste of Computing Increase Computer Science Enrollment?

The Exploring Computer Science (ECS) high school curriculum is designed to foster deep engagement through equitable inquiry around computer science concepts. We have shown that students find ECS courses personally relevant, are increasing their expectancies of success and perceived value for the field of computer science, and are more likely to take another computing course

A longitudinal analysis of pathways to computing careers: Defining broadening participation in computing (BPC) success with a rearview lens

Efforts to increase the participation of groups historically underrepresented in computing studies, and in the computing workforce, are well documented. It is a national effort with funding from a variety of sources being allocated to research in broadening participation in computing (BPC). Many of the BPC efforts are funded by the National Science Foundation (NSF) but as existing literature shows, the growth in representation of traditionally underrepresented minorities and women is not commensurate to the efforts and resources that have been directed toward this aim. Instead of attempting to tackle the barriers to increasing representation, this dissertation research tackles the underrepresentation problem by identifying what has worked (leveraging existing real-world data) to increase representation. This work studies the educational pathways of persons who have successfully transitioned into the computing workforce and identifies the common roadmaps that have contributed to retention, persistence, and success in attaining computing employment. Descriptive statistics, Logistic regression, Classification algorithms, Clustering, and Predictive analytics were employed, using the Stata statistical tool and Orange Data Mining tool on real-world data, to identify educational pathways that have resulted in successful employment outcomes for women and blacks in computing. The results of this analysis have highlighted key information that is capable of informing future “Broadening Participation in Computing” (BPC) efforts. This is because the information will enable researchers and decision makers to have a clearer picture of what educational choices have resulted in favorable outcomes for underrepresented minorities and women in computing; and consequently, researchers and decision makers would be able to more accurately target their BPC efforts to achieve optimal results. This knowledge can also be applied in career advising for young students who are trying to chart their path into computing, providing insight into alternative pathways

Female computer science and engineering undergraduates: reflections on participation in the academic landscape

Women continue to be underrepresented in computer science and technology related fields despite their significant contributions. The lack of diversity in technology related fields is problematic as it can result in the perpetuation of negative stereotypes and closed-minded, unchecked biases. As technology tools become integral to our daily lives it is essential that a diverse group of people contribute to the sociocultural environments where we participate and live. This dissertation is a phenomenological, interview-based, study designed to investigate the lived experience of women in undergraduate computer science and engineering programs. The purpose of this study was to better understand the factors that might encourage or discourage the participation women in the major and in the field. In order to grow the number of women in technical fields it is important to first understand what attracts them to the field and what supports they find helpful or not helpful. This study illuminated some recommendations that might guide the work of practitioners in secondary schools as well as higher education. Among other things, participants appreciated being challenged by the content and assignments, feeling support from faculty and peers, feeling a connection to the culture, effective encouragement to persist, and engaging interactions. All of the participants described having gone into their field to make a positive impact on society and they also all described the importance having at least one supportive female mentor. Participants described the importance of having spaces where they felt included and appreciated their professors and peers who pushed back against the historical CS-world stereotypes. While the experience of each participant was unique, and there were some very negative experiences, all six participants reported having mostly positive experience in their undergraduate programs

Examining the role of cultural capital in access and equity for female C-STEM learners of color

This study aimed to examine the impact of cultural capital (Bourdieu, 1973; Bourdieu & Passeron, 1977) on the access and equity of C-STEM learners of color under the following four tenets of Yosso’s (2005) Community Cultural Wealth model: aspirational, familial, social, and navigational capital. A theoretical framework of Critical Race Theory (CRT; Crenshaw, 1988) was utilized to frame the study. Research questions explore the impacts of cultural capital upon the perceived access and equity of female C-STEM learners. Qualitative methodology guided the study under the integrated approach of Quantitative Ethnography (QE) which examines qualitative data with the support of statistical analysis. This was done utilizing the Epistemic Network Analysis (ENA) webtool. Discourse analysis provided the researcher with a lens through which data from participants’ interview responses were analyzed. As a retrospective study, the researcher sought to explore the impact of participants’ cultural background on their educational and career trajectories in C-STEM. Participants consisted of fifteen women of color who self-identified is Black/African American, Hispanic/Latina, and Korean-American with degrees in computer science and closely related C-STEM fields. Interviews were conducted via Zoom from March 2023-July 2023. Each interview lasted for approximately 60 minutes. Data was transcribed and analyzed by three coders (including the researcher) as a form of interrater reliability. Initial codes for the study were as follows: access, equity, aspirational capital, familial capital, social capital, and navigational capital. Emergent codes of cultural, financial, and spiritual resources emerged from the data. Key findings indicate a positive relationship between the connection of access to aspirational, familial, social, and navigational capital. Strong connections were also identified with the emergent codes of cultural and financial support. The findings indicate that the female C-STEM learners of color in the study found encouragement in their journeys, were able to navigate various structures, and had the ability to thrive in community as a result of access to financial & cultural resources. Furthermore, the findings indicate the success that results from positive community networks for learners of color. Implications for future research explore developmental opportunities for organizational change in C-STEM learners from an assets-based lens

Computer Science at Community Colleges: Attitudes and Trends

This study aimed to understand the identity and attitude of students enrolled in computer science (CS) or programming-related course at community colleges nationwide. This study quantitatively evaluation data for estimating the relationships between students’ identity and attitudes toward computer science with prior programming experience and other demographic factors. I distributed the survey to community college faculty of computer science programs nationwide. Questions for this study were adapted from the Computing Attitude Survey developed by Weibe, Williams, Yang, & Miller (2003). Using two robust quantitative statistical methodologies, I investigated the correlations and predictability of previous programming experience, gender, race, and age with participants\u27 attitudes toward computer science. This study drew its inspiration from prior works of Dorn and Tew (2015) and Chen, Haduong, Brennan, Sonnert, and Sadler (2018), whose studies looked at previous experiences in programming with a favorable attitude toward computer science. The primary independent variable was a students’ prior programming experience. Under evaluation, the dependent variables were students\u27 programming experience and demographic characteristics such as race, gender, and age. This investigation showed a significant association between programming experience and attitude toward computer science. Among the demographic variables evaluated, students\u27 racial identity was the only factor found highly correlated with attitudes toward computer science. Future work will consider the association between participants\u27 accumulated college credit hours and specific programming language effects on computer science attitudes

Increasing the Enrolment of Women in IT at Fanshawe College

Women comprise over 50% of the population of Canada. At Fanshawe College, where I am a female professor in the School of Information Technology, approximately 95% of the students in my classes are male. The number of women enrolling in Information Technology (IT) programs has been declining in recent years, although enrolment of females in other post-secondary programs is increasing. Technology is becoming ever-present in the lives of young people, including girls and young women, yet education and employment in technology fields are not attracting women

Technical Interviews: Another Barrier to Broadening Participation in Computing?

What does it take to obtain a computing position in the industry? Although anecdotal reports state that ``hiring is broken,\u27\u27 empirical evidence is necessary to identify the flaws in the existing system. The goal of this dissertation was to understand what expectations companies have for job seekers in computing, and to explore students\u27 experiences with technical interviews and their pathways to job attainment. In particular, this work considered how hiring practices may impact populations already underrepresented in computing such as women, Black/African American students, and Hispanic/Latinx students. It also sought to understand how minoritized populations leverage their own inherent capital to overcome obstacles throughout the process. The theoretical frameworks of community cultural wealth, social cognitive career theory, identity theory, and intersectionality guided the studies, to answer the following research questions: 1) What does the hiring process in computing look like from both the applicant and industry perspective?; 2) How do cultural experiences impact technical interview preparation?; 3) How do technical interviews, and other professional and cultural experiences impact computing identity?; and 4) How do students describe their experiences with the hiring process in computing? To address these questions, a variety of methods were employed, beginning with a systematic literature review. This was followed by an explanatory sequential mixed-methods design that utilized a survey, statistical analysis, and semi-structured interviews. Discursive phenomenography was also the methodology chosen which shaped the qualitative inquiry. The findings illustrated the unique experiences and support mechanisms students from different gender, racial, and ethnic backgrounds utilize to succeed in hiring. These results not only serve to inform students, educators, and administrators how to best prepare for technical interviews, but also present a call to action for industry to change hiring and workplace practices that limit diversity. Suggestions and guidelines are given to enable a hiring process that can still achieve its target of finding qualified employees, but that does so in a manner more inclusive to all job seekers

STEM Community Chairs Progress Updates Spring 2016

The following is a brief update of the activities and efforts being undertaken by UNO’s Dr. George and Sally Haddix Community Chair of STEM Education as being held by Dr. Neal Grandgenett. The goal of this position is: Position Goal: To organize, lead and inspire collaborative STEM initiatives at UNO, that cross colleges and disciplines, and that aggressively position UNO to be a true national leader in interdisciplinary STEM programs. (Curriculum, Capacity, Collaboration

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space