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PurposeThis study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approachThis paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning. FindingsFindings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables. Originality/valueData science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data. 

The importance of data literacies and the shortage of research surrounding data science in elementary schools motivated this research-practice partnership (RPP) between researchers and teachers from a STEM elementary school. We used a narrative case study methodology to describe the instructional practices of one music teacher who co-designed a data science curricular unit during a summer professional development program and implemented it in her 5th-grade music classroom. Data collected for this study include in-person and video observations, reflective journals, artifacts, and interviews. Findings suggest that this teacher integrated data science literacies into her classroom by supporting multiple avenues for data storytelling and relying on learners’ everyday discourse and experiences. Our study details a practical example of implementing data science with non-STEM domains in elementary classrooms. 

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.