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Abstract Search engine algorithms are increasingly subjects of critique, with evidence indicating their role in driving polarization, exclusion, and algorithmic social harms. Many proposed solutions take a top-down approach, with experts proposing bias-corrections. A more participatory approach may be possible, with those made vulnerable by algorithmic unfairness having a voice in how they want to be “found.” By using a mixed methods approach, we sought to develop search engine criteria from the bottom-up. In this project we worked with a group of 16 African American artisanal entrepreneurs in Detroit Michigan, with a majority female and all from low-income communities. Through regular in-depth interviews with select participants, they highlighted their important services, identities and practices. We then used causal set relations with natural language processing to match queries with their qualitative narratives. We refer to this two-step process-- deliberately focusing on social groups with unaddressed needs, and carefully translating narratives to computationally accessible forms--as a “content aware” approach. The resulting content aware search outcomes place themes that participants value, in particular greater relationality, much earlier in the list of results when compared with a standard Web search. More broadly, our use of participatory design with “content awareness” adds evidence to the importance of addressing algorithmic bias by considering who gets to address it; and, that participatory search engine criteria can be modeled as robust linkages between interviews and semantic similarity using causal set relations. 

Computing is everywhere, and it's here to stay. Computing is crucial in many disciplines and influences every discipline. It’s unlikely we'll willingly return to a society unmediated by computing. How do our institutions proceed? This BoF asks, "Should computing be a requirement for all college and university students?" Some say yes, citing potential for improving equity-of-access, for expanding students' capabilities, for diversifying the people who understand and critique computing, and for increasing the diversity of computing participation. Some say no, citing the lack of equity-of-outcomes, the infeasibility of teaching all students equitably, and students' need for freedom in choosing what they study. Some say, "Let's consider the spectrum of possibilities... ." This session will discuss these possibilities, expressed and constrained by 2024's forces. Is computing's value saturated - or soon to be? Or is computing a meta-skill, whose practice in learning-to-learn amplifies individual efficacy along all paths? Is Computing1 too gate-kept to be as equitable a GenEd as Composition1? Or does requiring computing, in fact, help dismantle those gates? Can students adequately learn about core computing concepts via non-CS courses that use computing? What might required computing entail? We invite and welcome all with an interest in computing-as-degree-requirement, program-requirement, or GenEd offering. The session's seed materials will highlight evidence against the idea, for the idea, and across its vast, uncertain middle. Our BoF proposers include researchers and educators, both non-CS-requiring and CS-requiring, as well as non-CS-required and CS-required "educatees." Join us! 

Data literacy, an important goal for social studies education, involves teaching students how to comprehend, analyze, interpret, evaluate, create, and argue with data and data visualizations such as timelines, maps, and graphs. Digital data visualizations support rapid inquiry and explorations that would be difficult on paper - such as adding data to an existing data visualization or creating multiple data visualizations of the same variable at multiple times or places for easy comparison. The process of asking questions and finding answers through data exploration is an important part of social studies education. While the authors recognize that there are many online data visualization tools that can be used in classrooms, they designed their tools, DV4L and Timeline Builder, specifically for use in social studies classrooms and based on feedback from prospective and current teachers. The authors have taken care to include features that teachers said would be useful in social studies data inquiry lessons and activities. These are still in a prototype phase, but are free to use and online. 

Using teaspoon languages to integrate programming across myriad academic disciplines. 

Abstract In this study, support for teaching data literacy in social studies is provided through the design of a pedagogical support system informed by participatory design sessions with both pre‐service and in‐service social studies teachers. It provides instruction on teaching and learning data literacy in social studies, examples of standards‐based lesson plans, made‐to‐purpose data visualization tools and minimal manuals that put existing online tools in a social studies context. Based on case studies of eleven practicing teachers, this study provides insight into features of technology resources that social studies teachers find usable and useful for using data visualizations as part of standards‐ and inquiry‐based social studies instruction, teaching critical analysis of data visualizations and helping students create data visualizations with online computing tools. The final result, though, is that few of our participating teachers have yet adopted the provided resources into their own classrooms, which highlights weaknesses of the technology acceptance model for describing teacher adoption. Practitioner notesWhat is already known about this topicData literacy is an important part of social studies education in the United States.Most teachers do not teach data literacy as a part of social studies.Teachers may adopt technology to help them teach data literacy if they think it is useful and usable.What this paper addsEducational technology can help teachers learn about data literacy in social studies.Social studies teachers want simple tools that fit with their existing curricula, give them new project ideas and help students learn difficult concepts.Making tools useful and usable does not predict adoption; context plays a large role in a social studies teachers' adoption.Implications for practice and/or policyDesigning purpose‐built tools for social studies teachers will encourage them to teach data literacy in their classes.Professional learning opportunities for teachers around data literacy should include opportunities for experimentation with tools.Teachers are not likely to use tools if they are not accompanied by lesson and project ideas.