The information won't just sink in: Helping teachers provide technology‐assisted data literacy instruction in social studies

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. What is already known about this topic Data 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 adds Educational 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 policy Designing 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.

Using network visualizations to engage elementary students in locally relevant data literacy

“…Interpreting data visualizations can be intimidating without context/content understanding (Shreiner and Guzdial, 2022). Most statistics classes present data with minimal context; however, data derives meanings from contexts and interpreters (Acker and Bowler, 2018).…”

“…Data visualization and visualization literacy are also crucial components of any definition of data literacy (Deahl, 2014; Rubin, 2020). Visualizations enhance students’ comprehension and reasoning of complex phenomena by offering contextually-rich information for explanation and argumentation (Roberts et al , 2014; Shreiner and Guzdial, 2022). Researchers designing visualizations to support students in extracting and interpreting data patterns have found that visualization literacy facilitates data literacy development (Alper et al , 2017; Bishop et al , 2019).…”

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

“…Social studies as a context for data science is explored in Shreiner and Guzdial's (in press) paper. This study described using open educational resources and data visualization tools for supporting teachers in their efforts to teach data literacy in social studies and history classrooms.…”

A secret agent? K-12 data science learning through the lens of agency

“…While there is doubtless value in coming to similar findings despite using different research approaches, there is also value in different approaches to researching data science learning. We have found it beneficial to consider essential research by scholars not only with backgrounds in computer science and statistics education, for example, but also in the arts (Matuk et al, 2022), humanities (Vance et al, 2022), social studies (Shreiner & Guzdial, 2022), and everyday life and experiences (Gebre, 2022;Radinsky & Tabak, 2022;Vacca et al, 2022). Other scholars have argued for the importance of a broadly humanistic approach to data science education, one that recognizes that questions about who uses data overlap with questions about who has value and power (V. Lee et al, 2021;Philip et al, 2016).…”