A Call for a Humanistic Stance Toward K–12 Data Science Education

Data literacy has been suggested as an important competence that individuals need to succeed in a data-intensive society. However, there is no common understanding as to what data literacy entails and how it could be developed. Instructional emphasis on developing competence of individuals fails to capture learners' relationship to data in everyday life and limits what they can possibly achieve in data-rich environments. This paper critically reviews conceptualizations of data literacy in the literature with a focus in K-12 education. The analysis determined four orientations of data literacy: development of competence, inquiry with data, awareness of personal data and civic engagement. I proposed a broader conceptualization of data literacy that integrates conceptions, competencies and contexts. The study offers holistic and context-oriented framing of data literacy for researchers and educators.

Section: Discussionmentioning

confidence: 99%

Section: Conceptualizations Of Data Literacy In the Literaturementioning

confidence: 99%

Section: Conceptualizations Of Data Literacy In the Literaturementioning

confidence: 99%

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Conceptions and perspectives of data literacy in secondary education

Gebre

2022

“…Activities such as these can create opportunities for students to discuss data, race and power. Lee, Wilkerson et al (2021) provide examples of how bias can be built into popular lists such as 'best places to live' or rankings for universities and high schools. These rankings deserve interrogation and recognition that they are constructed based on human decisions.…”

Section: Examine Labels Categories Rankings and Claims About Authoritymentioning

confidence: 99%

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

Lee

Pimentel

Bhargava

et al. 2022

Self Cite

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.

“…In this way, work with data represents a relatively uncommon point of overlap between science and mathematics standards and across international borders. Common across these calls is that the data students analyse can be relevant to students; analysing relevant data can call on students to use a range of capabilities to answer questions or solve problems, applying ideas from science, mathematics, or computer science in the process (Kjelvik & Schultheis, 2019; Lee, Wilkerson, et al, 2021; Rosenberg, Lawson, et al, 2020).…”

Section: What Advances In Analysing Data Suggest For Science Teachers...mentioning

confidence: 99%

Big data, big changes? The technologies and sources of data used in science classrooms

Rosenberg

Schultheis

Kjelvik

et al. 2022

With improving technology and monitoring efforts, the availability of scientific data is rapidly expanding. The tools that scientists and engineers use to analyse data are changing in response. At the same time, science education standards have shifted to emphasize the importance of students making sense of data in science classrooms. However, it is not yet known whether these exciting new datasets and tools are used science classrooms, and what it would take to facilitate their use. To identify opportunities, research is needed to capture the data practices currently performed in classrooms, and the roles of technology for student learning. Here, we report findings from a survey conducted in the United States of 330 science teachers on the data sources, practices and technologies common to their classroom. We found that teachers predominantly involve their students in analysing relatively small data sets that they collect. In support of this work, teachers tend to use the technologies that are available to them—namely, calculators and spreadsheets. In addition, we found that a subset of teachers used a wide variety of data sources of varying complexity. We discuss what these findings suggest for practice, research and policy, with an emphasis on supporting teachers based on their needs. Practitioner notes What is already known about this topic Collecting and analysing data are central to the practice of science, and these skills are taught in many science classrooms at the pre‐collegiate (grades K‐12) level. Data are increasingly important in society and STEM, and types and sources of data are rapidly expanding. These changes have implications for science teachers and students. What this paper adds We found that the predominant data source science teachers use is student‐collected, small data sets. Teachers use digital tools familiar and available to them: spreadsheets and calculators. Teachers perceive the cost and time it would take to learn to use digital tools to analyse data with their students as key barriers to adopting new tools. Despite the predominance of small, student‐collected data analysed using spreadsheets or calculators, we also found notable variability in the data sources and digital tools some teachers used with their students. Implications for practice and/or policy Many of the changes called for in science education standards and reform documents, regarding how students should collect and analyse data, have not yet been fully realized in pre‐collegiate classrooms. Science teacher educators and science education researchers should build curricula and develop digital tools based on which kinds of data sources and digital tools teachers presently use, while encouraging more complex data useage in the future.