Boosting Data Science Teaching and Learning in STEM

Data fluency is the ability to navigate the world of data. This includes understanding the sources of data, structuring data for analysis, interpreting representations of data, inferring meaning from data, and explaining data and findings to diverse audiences. Data science is becoming more important as a career opportunity and a mechanism for addressing complex phenomena in STEM disciplines. This project addresses a critical need to help middle school teachers learn to incorporate data science in their teaching. It uses an open-source platform called the Common Online Data Analysis Platform (CODAP) as a tool for teachers to learn about data science and develop resources for students’ learning. We will develop a framework for teachers’ knowledge of data science teaching and learning. Insights from the project will help develop effective practices for teaching data science and understanding how students learn data science.

This project will result in two key products: a framework for teacher data fluency and a set of resources for teacher professional learning in data science, including cases of classroom practice that illustrate teaching and learning progressions in data science and surface common student roadblocks, materials for site-based Professional Learning Communities, and professional learning modules that engage teachers in the kind of data-rich learning called for by science education standards and STEM education more broadly. The project will include two stages. During stage one, the project will use a design-based research approach to develop a model of pedagogical content knowledge for data fluency in middle school. Stage one will answer the following questions: (1a) What do teachers need to know and be able to do to support students in becoming data fluent? (1b) What are common student misconceptions and roadblocks in students’ progress to data fluency? (1c) What are the core components of professional learning that boost teachers’ data fluency and their ability to support students becoming data fluent? During stage two, the project will use a mixed methods approach to study the model’s implementation. Stage two will address the following questions: (2a) What impact does professional learning with the core components identified in stage one have on the opportunities to learn teachers provide to their students and on their students’ data fluency? (2b) Are the professional learning innovations usable and feasible for the end users? (2c) In what ways do teachers’ and students’ classroom interactions reflect the model of pedagogical content knowledge developed in stage one? What evidence supports or refutes the hypothesis about the knowledge and skills teachers need to support students’ movement to data fluency?