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In this comparative case study, we used activity theory to explain how and why different middle-school STEM teachers from the same professional development community made different curricular adaptation choices for biomimetic design activities. Analysis of teacher interviews, classroom observations, and lesson artifacts revealed that teachers’ choices for biomimicry activities were particularly influenced by confidence with and access to particular tools and by rules related to learning goals and time constraints. 

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. 

What do high school students learn from a two-day datathon during which they tackle data to visualize the impact of biased data on healthcare decisions? How do they interact with their team of high school students, data scientists, clinicians, and teachers? What did we, the developers and leaders of the datathon, learn? How would we approach it differently next year? Our goal is to answer these questions plus share lessons learned. We will then divide the audience into teams to brainstorm ways to approach and solve some of the problems we experienced and hopefully recruit some audience members to participate in our June 2025 Brown University Health Artificial Intelligence (AI) Systems Thinking for Equity (HASTE) Datathon in Providence, Rhode Island (Brown University Datathon, 2024). 

There is a critical need for research-based active learning instructional materials for the teaching and learning of STEM in online courses. Every year, hundreds of thousands of undergraduate non-science majors enroll in general education astronomy courses to fulfill their institution’s liberal arts requirements. When designing instructional materials for this population of learners, a central focus must be to help learners become more scientifically and data literate. As such, we developed a new, three-part, curricular model that was used to inform the creation of active-learning instructional materials designed for use in online courses. The instructional materials were designed to help introductory astronomy students engage meaningfully with science while simultaneously improving their data literacy self-efficacy (especially as it pertained to making evidence-based conclusions when presented with a variety of data representations). We conducted a pilot study of these instructional materials at nine different colleges and universities to better understand whether students’ engagement with these materials lead to improved beliefs and self-efficacy. The results of our student survey analysis showed statistically significant changes on survey items that assessed students’ beliefs about science engagement, citizen science, and their data literacy skills. Additionally, we assessed whether faculty who implemented these materials were able to easily incorporate them into existing online astronomy courses. The instructor feedback emphasized that our curriculum development model did successfully inform the creation of easy-to-implement instructional materials, generating the potential for widespread dissemination and use at the undergraduate level. 

Robotics activities can provide students with opportunities to engage in computational thinking (CT) as well as support disciplinary learning goals. The goal of the Robots in Science project is to create, implement, and refine a PD program for middle school science teachers to design and implement robotics and CT-integrated science lessons. Two case studies illustrate how teachers used robotics activities to provide opportunities for science learning. 

Robotics activities can provide students with opportunities to engage in computational thinking (CT) as well as support disciplinary learning goals. The goal of the Robots in Science project is to create, implement, and refine a PD program for middle school science teachers to design and implement robotics and CT-integrated science lessons. Two case studies illustrate how teachers used robotics activities to provide opportunities for science learning.