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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. 

Computational thinking (CT) is key in STEM and computer science (CS) education. Recently, there has been a surge in studies inquiring about the factors that predict the CT development of young students. We extend these prior works by inquiring about the factors that predict the CT of students (n = 932) in a constructionist game-based learning (GBL) STEM curriculum. Specifically, after addressing missing data through imputation, we apply Multilevel Modeling (MLM) to identify these potential factors in Scratch games and students’ CT. We found that teachers’ experience implementing game-based curricula, students’ Scratch experience, student choice of game genre, and the interaction between teacher experience and game genre significantly predicted CT. Instead, students’ gender did not emerge as a significant predictor of CT. We provide recommendations for curricula that support CT through constructionist GBL. 

The Innovate to Mitigate (I2M) project poses challenges for secondary-school students to design feasible, innovative strategies that mitigate CO2 emissions and thus global warming. Design is informed by research on problem-based learning, pedagogy for which poses demands on teachers. This paper presents preliminary evidence about how I2M teachers supported student teams to engage in science and engineering practices. 

The purpose of science competitions or science fairs in STEM education is to provide students with opportunities to experience and practice science as it is practiced and experienced in the real world. The Innovate to Mitigate project hosts an annual open innovation challenge for students aged 13-18 to develop methods for mitigating global warming. Over several weeks, students innovate, develop prototype solutions, and engage with peers and with scientists online about their developing ideas. Finally, they submit videos and papers for discussion and judging by a panel of scientists. Submissions over the past few years have included projects over a wide range of domains, for example, energy conservation, renewable energy, agricultural innovations, or social/behavioral change. Framing learning goals for science fairs and science competitions around phenomena that are meaningful to young people offers the opportunity to make direct connections to relevant science and to understand how science is useful in society. We offer suggestions based on what we have learned that provide multiple ways for teachers to begin to support students in learning and effectively using the science practices. Carefully designed competition environments can reveal students as effective problem-solvers, unleash their imaginations, and help them to innovate. 

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.