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1. From Data Collectors to Data Producers: Shifting Students’ Relationship to Data

   https://doi.org/10.1080/10508406.2019.1678164  

   Hardy, Lisa; Dixon, Colin; Hsi, Sherry (January 2020, Journal of the Learning Sciences)
2. Bringing Students’ Lives Into Data Science Classrooms

   https://doi.org/10.1162/99608f92.6d2aec03  

   Weintrop, David; Israel-Fishelson, Rotem (April 2024, Harvard data science review)
3. Engaging elementary students in data science practices

   https://doi.org/10.1108/ILS-06-2023-0062  

   Adisa, Ibrahim Oluwajoba; Herro, Danielle; Abimbade, Oluwadara; Arastoopour_Irgens, Golnaz (December 2023, Information and Learning Sciences)

   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. 

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4. Functional Data Science for Secondary-School Students

   https://doi.org/10.15388/infedu.2024.24  

   Biberstein, Paul; Castleman, Thomas; Chen, Luming; Krishnamurthi, Shriram (December 2024, Informatics in Education)

   CODAP is a widely-used programming environment for secondary school data science. Its direct-manipulation–based design offers many advantages to learners, especially younger students. Unfortunately, these same advantages can become a liability when it comes to repeating operations consistently, replaying operations (for reproducibility), and also for learning abstraction.In response, we have extended CODAP with CODAP Transformers, which add a notion of functions to CODAP. These provide a gentle introduction to reuse and abstraction in the data science context. We present a critique of CODAP that justifies our extension, describe the extension, and showcase some novel operations. Our extension has been integrated into the CODAP codebase, and is now part of the standard CODAP tool. It is already in use by the Bootstrap curriculum. 

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5. Preparing students to meet their data: an evaluation of K-12 data science tools

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   Israel-Fishelson, Rotem; Moon, Peter F; Tabak, Rachel; Weintrop, David (December 2023, Behaviour & Information Technology)