Big data, big changes? The technologies and sources of data used in science classrooms. (13th June 2022)

• Record Type: Journal Article
• Authors: Rosenberg, Joshua M.; Schultheis, Elizabeth H.; Kjelvik, Melissa K.; Reedy, Aaron; Sultana, Omiya
• Is Part Of: British journal of educational technology. Volume 53:Number 5(2022)
• Page Start: 1179
• Page End: 1201
• Publication Date: 2022-06-13
• Subjects: analysing and interpreting data -- data literacy -- data science -- science education -- survey research methods; Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.13245
• Languages: English
• ISSNs: 0007-1013

Abstract

Abstract: 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.