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We examine how developers of data science curricula determine what makes a pedagogically effective dataset enabling 10–14 year-old students (“middle school” in the United States) to engage in the data investigation cycle by posing their own questions about relationships among variables. We describe strategies for curating existing datasets to address goals for learning about data, and for optimizing the use of these datasets once they are curated. We investigate how data science educators can transform existing datasets into ones appropriate for students with little data experience, drawing on our experience working with several publicly available datasets, which students explored in CODAP (the Common Online Data Analysis Platform).
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This content will become publicly available on December 10, 2025
Functional Data Science for Secondary-School Students
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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- Award ID(s):
- 2208731
- PAR ID:
- 10585424
- Publisher / Repository:
- Vilnius University
- Date Published:
- Journal Name:
- Informatics in Education
- ISSN:
- 1648-5831
- Page Range / eLocation ID:
- 723 to 734
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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