Exploring Elementary Teachers' Perceptions of Data Science and Curriculum Design through Professional Development

Data science and computational thinking (CT) skills are important STEM literacies necessary to make informed daily decisions. In elementary schools, particularly in rural areas, there is little instruction and limited research towards understanding and developing these literacies. Using a Research-Practice Partnership model (RPP; Coburn & Penuel, 2016) we conducted multimethod research investigating nine elementary teachers' perceptions of data science and related curriculum design during professional development (PD). Connected Learning theory, enhanced with Universal Design for Learning, guided ways we assisted teachers in designing the data science curriculum. Findings suggest teachers maintained high levels of interest in data science instruction and CT before and after the PD and increased their self-efficacy towards teaching data science. A thematic analysis revealed how a data science framework guided curriculum design and assisted teachers in defining, understanding, and co-creating the curriculum. During curriculum design, teachers shared the workload among partners, made collaborative design choices, integrated differentiation strategies, and felt confidence towards teaching data science. Identified challenges included locating data sets and the complexity of understanding data science and related software. This study addresses the research gap in data science education for elementary teachers and assists with successful strategies for data science PD and curricular design.