7: Disciplinary identities

Disciplinary identities are one way of thinking about learning in terms of identity. This week we focus on disciplinary identities within formal learning environments such as K-12 classrooms.

Weekly update

This video responds to a Week 6 discussion report on diSessa's Changing Minds, gives a mini-lecture on disciplinary identity, and then introduces this week's readings.

Summary

• Discussion report. Computer literacy (knowing how to work the machine) is not what we mean by computational literacies, which are a superset of the formal study of computer science. Discussions of ethics and politics in computing are richer when they are grounded in technical ideas. Schools are now full of computers, but that has not produced the empowered computational literacy diSessa hoped for. Comparing AI literacy to learning to drive a car raises the question of whether we are teaching people to drive or asking them to open up the hood; neither is very satisfying. Imagining how computing could serve the best we can manage in schools points to design-based research: studying things as they could be, not just as they are.
• Disciplinary identity. Becoming a computer scientist means becoming a certain kind of person, with assumptions about who belongs. Key constructs include interest, belonging, self-efficacy, and recognition. A subject position is a model of personhood available in a social world; authoring an identity is a push and pull between who you would like to be and the expectations and routines already in place.
• Studying and responding to disciplinary identity. Methods include CS autobiographies, "draw a computer scientist," and longitudinal studies following learners across settings. Responses include affinity-based programs such as COMPUGIRLS, justice-centered computing, and e-textiles, which bring culturally feminine-coded practices into computing. Research has documented anti-Blackness in computing and computing education, and these questions extend to the subject positions modeled by large language models, for example through AI auditing with youth.
• Lewis et al. (2016). A cleanly structured study of how students decide whether computer science is "for me," a fragile decision with big consequences in high school. Students compare themselves to four stereotypes: CS is singularly focused, asocial, competitive, and male.
• Vogel (2021). Brings translanguaging, the recognition that people move fluidly across language practices, into computer science education, against a long history of linguistic oppression in US schools. Critical translingual computing education argues that language is not a side issue of broadening participation, but connected to the core of what we think computer science is and what it is for.

Readings

Lewis, C. M., Anderson, R. E., & Yasuhara, K. (2016). “I don't code all day”: Fitting in computer science when the stereotypes don't fit. Proceedings of the International Computer Science Education Research Workshop (pp. 23–32).

Vogel, S. (2021 , December). “Los Programadores Debieron Pensarse Como Dos Veces”: Exploring the Intersections of Language, Power, and Technology with Bi/Multilingual Students. ACM Transactions on Computing Education, 21(4), 1–25. doi:10.1145/3447379