The Nature of Analyzing and Interpreting Data in 6-12 Science Education

This dissertation explores the nature of analyzing and interpreting data in 6-12 science education, focusing on how it’s taught and how it can be improved to meet the demands of the 21st century. It examines instructional practices, challenges, and curriculum content to provide a comprehensive understanding of data literacy in secondary science education. It is structured as an introduction chapter, three research chapters, and a conclusion chapter, each addressing different aspects of data literacy. The second chapter investigates how 6-12 science teachers define and teach data analysis and interpretation. This study uses focus groups to gather insights into teachers' approaches and influencing contextual factors. It also examines the alignment of their practices with a synthesized model of best practices for data literacy in science education. The third chapter identifies the obstacles teachers face in teaching data literacy, such as insufficient training, lack of resources, and curriculum limitations. This investigation uses the same focus group methodology as Chapter 2 but with a different focus on perceived teacher challenges and their impact on instructional practices. The fourth chapter evaluates a 7th-grade unit from an open-source science curriculum to assess how it incorporates data literacy. The methodology involves a detailed content analysis of the curriculum materials, focusing on the types of data used, the data analysis methods taught, and their alignment with modern data practices. This approach allows for a thorough examination of the curriculum's strengths and weaknesses in promoting data literacy. The dissertation concludes with a fifth chapter that synthesizes the findings across the three studies and offers recommendations for improving data literacy instruction in 6-12 science education. This dissertation highlights the need for a comprehensive and integrated approach to teaching data literacy. It offers valuable insights and actionable recommendations for educators, curriculum developers, and policymakers to enhance science education and better equip students with data-driven skills.