'It Can Relate to Real Lives': Attitudes and Expectations in Justice-Centered Data Structures & Algorithms for Non-Majors

Prior work has argued for a more justice-centered approach to postsecondary computing education by emphasizing ethics, identity, and political vision. In this experience report, we examine how postsecondary students of diverse gender and racial identities experience a justice-centered Data Structures and Algorithms designed for undergraduate non-computer science majors. Through a quantitative and qualitative analysis of two quarters of student survey data collected at the start and end of each quarter, we report on student attitudes and expectations. Across the class, we found a significant increase in the following attitudes: computing confidence and sense of belonging. While women, non-binary, and other students not identifying as men (WNB+) also increased in these areas, they still reported significantly lower confidence and sense of belonging than men at the end of the quarter. Black, Latinx, Middle Eastern and North African, Native American, and Pacific Islander (BLMNPI) students had no significant differences compared to white and Asian students. We also analyzed end-of-quarter student self-reflections on their fulfillment of expectations prior to taking the course. While the majority of students reported a positive overall sentiment about the course and many students specifically appreciated the justice-centered approach, some desired more practice with program implementation and interview preparation. We discuss implications for practice and articulate a political vision for holding both appreciation for computing ethics and a desire for professional preparation together through iterative design.
Comment: Experience Reports and Tools paper in the Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024); 7 pages