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This paper examines design decisions of a team seeking to support students’ working with data in a standards-based high school biology curriculum. The team’s decisions required them to balance four goals that often came into tension during development: (1) helping students meet performance expectations specified in the targeted standards; (2) engaging students with extant datasets; (3) supporting student sensemaking; and (4) supporting coherence from the student point of view. Efforts to balance these goals in design revealed the limitations of existing science standards for adequately supporting students’ work with extant datasets and for developing students’ skill in covariational reasoning. Achieving the goals of supporting student sensemaking in science requires more intensive support for building the conceptual foundations of statistical concepts when developing a grasp of the practice of using mathematics in science.
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This content will become publicly available on September 15, 2026
Balancing Design Goals for Supporting Students’ Work with Extant Data in Science
This paper examines design decisions of a team seeking to support students’ working with data in a standards-based high school biology curriculum. The team’s decisions required them to balance four goals that often came into tension during development: (1) helping students meet performance expectations specified in the targeted standards; (2) engaging students with extant datasets; (3) supporting student sensemaking; and (4) supporting coherence from the student point of view. Efforts to balance these goals in design revealed the limitations of existing science standards for adequately supporting students’ work with extant datasets and for developing students’ skill in covariational reasoning. Achieving the goals of supporting student sensemaking in science requires more intensive support for building the conceptual foundations of statistical concepts when developing a grasp of the practice of using mathematics in science.
more »
« less
- Award ID(s):
- 2031459
- PAR ID:
- 10644761
- Editor(s):
- Schunn, Chris
- Publisher / Repository:
- ISDDE
- Date Published:
- Journal Name:
- Educational Designer
- ISSN:
- 1759-1325
- Subject(s) / Keyword(s):
- NGSS extant data data analysis design decisions
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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