I’m presenting a paper, open educational resource, panel, and poster at SIGCSE TS 2022 focusing around justice-centered approaches to CS in higher education. Several authors from the University of Washington are thinking about starting a community of practice: to receive more information in the coming weeks, add the Paper Session: Ethics: Proposals and Counternarratives to your agenda.

My prior work at RESPECT 2021 proposed affordance analysis as a more critical algorithm analysis centering the limitations, implications, and unjust outcomes when we apply data structures and algorithms to “solve” problems. This work presents the broader context and end purposes for engaging critical perspectives in our university classrooms by engaging questions like, “What do we ultimately want students to take away from our CS education?” See the CSE 373 Winter 2022 course website and course project on GitHub.

  1. Autocomplete data structures and algorithms for search suggestions and DNA indexing.
  2. Priority queue data structures for content moderation and shortest paths.
  3. Shortest paths and graph data structures for seam carving and navigation directions.

Justice-centered approaches to CS education emphasize 3 features: the content of curriculum (centering ethics), the design of learning environments (centering identity), and the politics and purposes of CS education reform.1 We should consider not only ethics, but also identity and political vision in our work.

  1. Sepehr Vakil. 2018. Ethics, Identity, and Political Vision: Toward a Justice-Centered Approach to Equity in Computer Science Education.