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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. 

In response to reform efforts to center students’ interest and identity in the context of assessment, we pilot tested a set of three-dimensional, phenomenon-driven assessment tasks and asked students to respond to additional items that prompted them to reflect on their experience with the tasks. Using a qualitative approach, we analyzed written responses from 502 middle school students across the United States. Through inductive and deductive coding, we developed a comprehensive framework that captures students' experiences across five dimensions: cognitive engagement, affective engagement, relevance to students’ lives, beyond-classroom connections, and assessment design and features. By exploring these dimensions, the framework aims to provide a comprehensive lens for understanding how students experience science assessments, revealing key insights into the factors that influence their engagement and learning. Ultimately, this framework can serve as a practical tool for educators and researchers to analyze and improve science assessments by centering students' voices, thereby fostering deeper learning, promoting student agency, and supporting all learners. 

The emphasis on an equitable vision of science learning in current science education reform efforts sees students as contributing to knowledge-building through drawing on their rich cultural and linguistic backgrounds while engaging in the three dimensions to make sense of compelling, relevant phenomena. However, this vision will not be fully realized without coherence between curriculum, instruction, and assessment. As a majority of states have now adopted standards aligned to or adapted from the Framework, we see an urgent need for assessments that can support rather than conflict with equitable science learning. In this study, we seek to understand the current state of Framework-aligned assessment tasks. We have amassed 352 middle school tasks, originating from state-level assessment banks and assessment developers at universities or research organizations. Our preliminary findings from characterizing 104 tasks revealed that the majority of tasks target dimensions of the NGSS or Framework-based standards and include a phenomenon. However, there are challenges in framing phenomena that attend to students’ interests and identities and engage students in three-dimensional sensemaking. Additionally, some phenomena are not based in real-world observations and are not authentic from students’ perspectives, which makes it difficult for students to see connections of local or global relevance. 

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.