Learning, Identity, and Power: Tensions and Possibilities in Equity-Oriented Computer Science Education

Computer science is rapidly emerging as a distinct feature of K-12 public education in the United States. Calls to expand computer science education are often linked to equity and diversity concerns around expanding access to girls and historically underrepresented students of color. In this dissertation, I argue that in addition to expanding access to the field, equity-oriented researchers and educators must also attend to how dominant discourses and ideologies are shaping the character of computer science education. Through a mixed-methods study combining ethnographic and social design experiment approaches, I examine (a) the current state of computer science education at a large, racially diverse high school in the San Francisco Bay Area, and (b) possibilities and tensions for computer science learning rooted in critical pedagogy and social justice traditions. The dissertation is organized as three distinct articles. Chapter 2 reviews extant literature in the field and advances a framework for computer science education rooted in sociopolitical theorizations of equity. In this chapter I also provide a case study and introduction to the Computer Science and Technology (CST) Academy, where studies presented in the next two articles are also based. Chapters 3 and 4 report on a social design experiment that provided students an opportunity to create socially relevant technology that addressed educational equity issues in their school. In Chapter 3, I draw on student surveys, artifacts (final project portfolios, student sketches, memos, presentations, and posters), artifact-based interviews, and field notes, to analyze the complex interplay between students’ social identities and disciplinary identities in computer science. I argue that the kinds of learning opportunities provided in computer science classrooms have significant implications for how students come to view their own social identities and futures within the discipline. In Chapter 4, drawing upon video data of a particular episode from the class, I argue that a conflict between a white male student and a Black female student was rooted in a lack of trust and solidarity between the students. The conflict and other moments of tension between students limited opportunities for collective learning and action, and more critically, led to the Black student and other students of color experiencing discomfort and feeling violated. Ultimately, I argue that in addition to expanding curriculum to include culturally relevant or social justice topics, equity-oriented approaches must also attend to the quality of student relationships, particularly in racially diverse contexts. Taken together, the articles in this dissertation contribute to a vision of computer science education rooted in educational equity and social justice traditions. This research has implications for the design of computer science learning contexts that aim to prepare young people to address the increasingly complex local, global, environmental, human rights and sociopolitical issues of the 21st century

Data literacies and social justice: Exploring critical data literacies through sociocultural perspectives

The ability to interpret, evaluate, and make data-based decisions is critical in the age of big data. Normative scripts around the use of data position them as a privileged epistemic form conferring authority through objectivity that can serve as a lever for effecting change. However, humans and materials shape how data are created and used which can reinscribe existing power relations in society at large (Van Wart, Lanouette & Parikh, 2020). Thus, research is needed on how learners can be supported to engage in critical data literacies through sociocultural perspectives. As a field intimately concerned with data-based reasoning, social justice, and design, the learning sciences is well-positioned to contribute to such an effort. This symposium brings together scholars to present theoretical frameworks and empirical studies on the design of learning spaces for critical data literacies. This collection supports a larger discussion around existing tensions, additional design considerations, and new methodologies