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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. 

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. 

Working with existing data is central to science investigations, but students and educators have generally not had experience using existing data sets to answer their own questions. We introduce a teaching routine that makes explicit critical steps in the process of working with data to gain insight into real-world phenomena. We intend the routine to support both curriculum developers and teachers in designing and enacting lessons to support students in engaging productively with scientific data, focusing on steps that are not commonly encountered in science classes. 

A core practice of science is planning and conducting investigations. This practice needs reconceptualizing, to account for where work happens between identifying a phenomenon and designing an investigation, and between gathering and analyzing data to support developing an explanation of that phenomenon (Manz et al., 2020). Teachers, supported by curriculum materials, need to engage students in becoming more involved in the decisions related to what data to choose as evidence, how to represent data to answer specific questions, and what conclusions can be drawn from data. We present results of a design study in which students investigated a dataset to answer a question about a major change to an ecosystem, using a technology tool, CODAP. We explore how the curriculum and teacher supported students in taking up different facets of data practices that support figuring out a phenomenon while moving between investigating and developing explanatory models. 

Abstract The data sets used in statistics education have changed over time, from mathematically “well‐behaved” ones that facilitated computation, to more context‐rich sources and now, with the increasing influence of data science practices, to “found” data, often from open data sites. As data sources change, it is important for educators to take a fresh look at the ways we engage students in thinking about the processes that generated the data they encounter. The use of already collected data requires particular attention because many of the decisions that went into the processes of obtaining the data are hidden. Students need to learn to ask “Who, When, How, Where, and Why?” data were collected and to wonder if the data really measure what needs to be measured. Our advocacy in this paper is to deepen the educational treatment of data production to better reflect the current and future practice of statistics and data science.