Learning How to Help Middle Grades Science Teachers Integrate Data Exploration and Sensemaking in the Classroom (Collaborative Research: Griffith)

Data literacy is the ability to ask questions, analyze, interpret, and draw conclusions from data. As the world and the workplace become more data-driven, students need to have stronger data literacy across multiple disciplines, including science. This project uses an instructional framework, Data Puzzles, to investigate how to support middle grades teachers learning to include data literacy in their science teaching. Data Puzzles integrate mathematical and computational thinking with ambitious science teaching instructional practices and contemporary science topics. Students engaging with Data Puzzles resources can analyze real-world climate science data using web-based data analysis tools to make sense of science phenomena and develop data literacy. An emphasis across topics is on how uncertainty influences data analyses and the strategies and tools used to make sense of data. Teachers in the project will learn about data literacy, data analysis and data science tools, and how to integrate them with science content.

The project builds on prior work to design the Data Puzzles instructional framework to develop teacher professional learning resources and models for supporting data literacy and sensemaking. A primary goal is helping teachers to learn how to confidently integrate data literacy and sensemaking in science teaching. The design-based research study includes mixed methods data to document the professional learning experience and students’ experience using the modules in classrooms. The study of teacher learning includes self-efficacy and teaching vision surveys, video of professional learning sessions, artifacts, and interviews. The study of students’ learning includes interviews and surveys with students. The project will develop resources for teacher and student learning that can be shared with researchers and educators.