Taking data feminism to school: A synthesis and review of pre‐collegiate data science education projects

Abstract

As the field of K‐12 data science education continues to take form, humanistic approaches to teaching and learning about data are needed. Data feminism is an approach that draws on feminist scholarship and action to humanize data and contend with the relationships between data and power. In this review paper, we draw on principles from data feminism to review 42 different educational research and design approaches that engage youth with data, many of which are educational technology intensive and bear on future data‐intensive educational technology research and design projects. We describe how the projects engage students with examining power, challenging power, elevating emotion and lived experience, rethinking binaries and hierarchies, embracing pluralism, considering context, and making labour visible. In doing so, we articulate ways that current data education initiatives involve youth in thinking about issues of justice and inclusion. These projects may offer examples of varying complexity for future work to contend with and, ideally, extend in order to further realize data feminism in K‐12 data science education. What is already known about this topic Data feminism is an emergent framework for changing data practices and discourse in service of equity and justice. Data science education is rapidly growing as a topic of interest in the educational technology research and design communities. Many educational technology and design projects have been launched and shared in publications that preceded the widespread distribution of the data feminism framework. What this paper adds Data feminism is partially re‐articulated in terms familiar to educational technology research communities. Prior and recent projects are organized with respect to how they illustrate potential connections to core data feminism principles. This paper identifies specific strategies that recent projects have used that have potential for realizing data feminism principles. Implications for practice and/or policy Educational technologists can use the re‐articulated principles of data feminism for education to inform their future design work. Tractable steps to achieve data justice that are attainable within existing educational systems can be pursued. Communities can and should bring together multiple ways of knowing to support new educational practices and futures with data.

Meaning making and relatedness: exploring critical data literacies on social media

“…We compared videos that showed one of the data practice themes identified in RQ1 (compiling data, situating data, and positioning the selves as data) and those without such practices. This analysis shared the premise with Lee et al ’s (2022) discussion that technologies to layer multimedia and data sources can elevate lived experiences and support data practices. Figure 3 displays the distribution of video features by data practices, and the brackets show chi-squared tests that examined video features by data corpora with and without data practices.…”

“…In envisioning how to promote critical data practices, Calabrese Barton et al (2021) discussed the notion of “textured fluidity” to focus on the dynamic nature of personal experiences with data as they collect, disseminate, and make sense of data in their lives and communities. Recognizing textured fluidity means acknowledging that personal and local experiences, narratives, and emotions might be as meaningful as big data (Calabrese Barton et al , 2021; D'ignazio and Klein, 2020; Lee et al , 2022). Our research illuminates how learning environments may provide opportunities to articulate experiences and build on others’ discourse, to highlight the validity of lived experiences.…”

“…To ground the research, we draw from scholarship in media literacy and communication (Hautea et al , 2017; Literat, 2021; Pangrazio and Selwyn, 2019) and education (Calabrese Barton et al , 2021; Lee et al , 2022; Louie, 2022) to define data literacies and critical data practices. We further review how social media platform features can support critical data practices (Barta and Andalibi, 2021; Mills et al , 2018; Stahl and Literat, 2022).…”

Keeping pace with innovations in data visualizations: A commentary for mathematics education in times of crisis

“…Emerging guidelines and research in statistics, data literacy, and data science education acknowledge the rise of innovative DVs (Bargagliotti et al, 2020 ) and encourage students to explore and create new means of visual expression that more fully capture lived experiences (Lee et al, 2022 ). Our commentary adds to this work by considering particular affordances of innovative DVs while situating how they might elicit emotion and experience as potentially generative aspects of making sense of crises through a mathematical lens.…”

Data science education across the disciplines: Underexamined opportunities for K‐12 innovation

“…A second cross‐cutting review, by Lee et al (2022) spotlights the humanistic (Lee et al, 2021) sides of data science. Using D'Ignazio and Klein's (2020) pioneering work as a guide, Lee and colleagues reviewed extant educational research projects that engaged learners with data through the lens of seven principles of data feminism , including examine and challenge power , consider context and make labour visible .…”