Worker-Driven Computing Education
Michael Lachney, Brian Ferguson Bey, Samuela Mouzaoir, C. A. Robinson · 2024
Culturally responsive computing scholars often use the metaphor and heuristic of intersectionality to address issues of race and gender in research with Black, Brown, and Indigenous children in U.S. K12 computer science (CS) education. This has led to critical analyses that reveal the inequitable conditions that children of color face in CS education. Intersectionality has also supported curricular innovations and interventions that aim to make CS education more equitable by being responsive to children's identities and communities. However, the concept of class has received less attention than race and gender by culturally responsive computing scholars who draw on intersectionality in their CS education research. How might class fit into intersectional research and theory in culturally responsive computing scholarship? The objective of this paper is to answer this question with a proof-of-concept study that tests the feasibility of what we call worker-driven computing education. We introduce findings from our research and collaborations with current, once, and retired autoworkers in Detroit, Michigan to define the computational thinking idea of automation from their perspectives. Through analyzing qualitative data, a worker-driven definition of automation was developed by drawing on autoworker-expertise about the points of production in the auto-industry. Our findings reveal how this definition focuses on introducing automation in the context of lifelong learning about how to communicate and work across divisions of labor to make workplaces safe, healthy, and efficient.