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Navigator to Producer: Data Science as a Access Point for Equitable Pre-College Computer Science Education (Poster 5)

Sun, April 16, 4:40 to 6:10pm CDT (4:40 to 6:10pm CDT), Radisson Blu Aqua Hotel, Chicago, Floor: 1st Floor, Atlantic E

Abstract

There have been notable advancements in pre-college computer science (CS) education over the last decades (Blikstein, 2018; Hendrick et al., 2021; The ACT Report, 2021). Successful initiatives such as CS4ALL, Code.org, and Exploring Computer Science (ECS) have broadened access and participation to CS education across the nation while also emphasizing a need for innovation in burgeoning areas—such as field developments at the intersection of CS and data science where fewer efforts have been placed for pre-college curriculum development. This is particularly relevant in cross disciplinary areas like data literacy—the knowledge and skill to extract, visualize, and critically analyze complex algorithms and data structures (Wilkerson & Polman, 2020). Mastery in these areas can have long lasting impacts on learners—including in such areas as self-efficacy, engagement and long term persistence. This is especially relevant among underrepresented groups (e.g., women, people of color, economically disadvantaged, etc.) where access disparities have persistent and often intergenerational consequences (Margolis et al., 2017; Santo et al., 2019).
While there are a growing number of curricular resources designed to support education around the “data revolution” (Lee & Wilkerson, 2018), where learners may access and “wrangle” complex data sources, there is often a lack of connection between what is relevant to the learner and the educational content (Lee et al., 2021). In essence, many existing frameworks do not provide topics and activities that empower or support student agency (Margolis et al., 2017; Santo et al., 2019)—in part, because the contexts and datasets used to frame learning are curated by others instead of drawn from learners themselves. Extant research across CS suggest learners are most successful when learning is situated in contexts that are familiar, relevant and driven by their personal interests, culture and sociopolitical milieu (Kafai et al., 2019; Ladson-Billings, 1995; 2014).
One solution to these issues is leveraging CS technologies and tools that center the learner in CS education experiences. In data science, this can be done using ubiquitous social media platforms such as Twitter, where it is possible for learners to draw on vast libraries of databases in relation to their social, cultural and sociopolitical interests. By engaging with data science, computer science and social media in these ways, we position the learners as producers of inquiry and content, rather than consumers or navigators of artifacts or interfaces generated by others.
In this study, we use mixed-methodological approaches to collect and analyze data drawn from high school level youth and adult educator participant (n=14) artifacts, interviews and survey responses generated during fourteen three-hour long curricular codesign sessions online. Insights gleaned provide initial insights into how stakeholders (e.g., researchers, youth and adult educators) responded and contributed to the co-construction of curriculum resources meant to support learner agency at the intersection of CS education, data science education, and culturally relevant pedagogies. We discuss our findings in relation to a growing CS curricular landscape where learner productions and cultural relevance are a salient part of engagement and where participatory research designs are quintessential to equity and success.

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