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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. 

Although the fields of educational data mining and learning analytics have grown in terms of the analytic sophistication and breadth of applications, the impact on theory-building has been limited. To move these fields forward, studies should not only be driven by learning theory but also the analytics should be used to inform theory. In this paper, we present an approach for integrating educational data mining models with design-based research approaches to promote theory-building that is informed by data-based models. This approach aligns theory, design of the learning environment, data collection, and analytic methods through iterations that focus on the refinement and improvement of all these components. We provide an example from our own work which is driven by a critical constructionist learning framework, the design and development of a digital learning environment for elementary-school aged children to learn about artificial intelligence within sociopolitical contexts, and the use of epistemic network analysis as a tool for modeling learning. We conclude with how this approach can be reciprocally beneficial in that educational data miners can use their models to inform theory and learning scientists can augment their theory-building practices through big data models.