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The role of computation in science is ever‐expanding and is enabling scientists to investigate complex phenomena in more powerful ways and tackle previously intractable problems. The growing role of computation has prompted calls to integrate computational thinking (CT) into science instruction in order to more authentically mirror contemporary science practice and to support inclusive engagement in science pathways. In this multimethods study, we present evidence for the Computational Thinking for Science (CT+S) instructional model designed to support broader participation in science, technology, engineering, and mathematics (STEM) pathways by (1) providing opportunities for students to learn CT within the regular school day, in core science classrooms; and (2) by reframing coding as a tool for developing solutions to compelling real‐world problems. We present core pedagogical strategies employed in the CT+S instructional model and describe its implementation into two 10‐lesson instructional units for middle‐school science classrooms. In the first unit, students create computational models of a coral reef ecosystem. In the second unit, students write code to create, analyze, and interpret data visualizations using a large air quality dataset from the United States Environmental Protection Agency to understand, communicate, and evaluate solutions for air quality concerns. In our investigation of the model's implementation through these two units, we found that participating students demonstrated statistically significant advancements in CT, competency beliefs for computation in STEM, and value assigned to computation in STEM. We also examine evidence for how the CT+S model's core pedagogical strategies may be contributing to observed outcomes. We discuss the implications of these findings and propose a testable theory of action for the model that can serve future researchers, evaluators, educators, and instructional designers.

 

In this experience report, we describe the Investigating Air Quality curriculum unit that integrates computational data practices with science learning in middle school science classrooms. The unit is part of the Coding Science Internship instructional model, designed to broaden access to computer science (CS) learning through scalable integration in core science courses, and through confronting barriers to equitable participation in STEM. In this report, we describe the core features of the unit and share preliminary findings and insights from student experiences in 13 science classrooms. We discuss affordances and challenges for student learning of computational data practices in formal science classrooms, and conclude with emerging recommendations for instructional designers. 

Contemporary science is a field that is becoming increasingly computational. Today’s scientists not only leverage computational tools to conduct their investigations, they often must contribute to the design of the computational tools for their specific research. From a science education perspective, for students to learn authentic science practices, students must learn to use the tools of the trade. This necessity in science education has shaped recent K–12 science standards including the Next Generation Science Standards, which explicitly mention the use of computational tools and simulations. These standards, in particular, have gone further and mandated thatcomputational thinkingbe taught and leveraged as a practice of science. While computational thinking is not a new term, its inclusion in K–12 science standards has led to confusion about what the term means in the context of science learning and to questions about how to differentiate computational thinking from other commonly taught cognitive skills in science like problem-solving, mathematical reasoning, and critical thinking. In this paper, we propose a definition ofcomputational thinking for science(CT-S) and a framework for its operationalization in K–12 science education. We situate our definition and framework in Activity Theory, from the learning sciences, in order to position computational thinking as an input to and outcome of science learning that is mediated by computational tools.

 

Growing awareness of both the demand for artificial intelligence (AI) expertise and the societal impacts of AI systems has led to calls to integrate learning of ethics alongside learning of technical skills in AI courses and pathways. In this paper, we discuss our experiences developing and piloting the TechHive AI curriculum for high school youth that integrates AI ethics and technical learning. The design of the curriculum was guided by the following pedagogical goals: (1) to respond to the capacity-building need for critical sociotechnical competencies in AI workforce pathways; and (2) to broaden participation in AI pathways through intentional instructional design to center equity in learning experiences. We provide an overview of the 30-hour learning sequence’s instructional design, and our “4D Framework,” which we use as a heuristic to help students conceptualize and inspect AI systems.We then provide a focused description of one of three chapters that make up the sequence. Finally, we present evidence of promise from an exploratory study of TechHive AI with a small sample of students, and discuss insights from implementation, including from our use of established resources for AI learning within the learning sequence as well as those created by our team. 

We will present emerging findings from an ongoing study of instruction at the intersection of science and computer science for middle school science classrooms. This paper focuses on student knowledge and dispositional outcomes in relation to a 2 week/10-lesson learning sequence. Instruction aims to broaden participation in STEM pathways through a virtual simulated internship in which students inhabit the role of interns working to develop a restoration plan to improve the health of coral reef populations. Through this collaborative work, students construct understanding of biotic and abiotic interactions within the reef and develop a computational model of the ecosystem. Analysis of pre/post surveys for n=381 students revealed that students who participated in the 2 week/10 lesson integrated computational thinking in science learning sequence demonstrated significant learning gains on an external measure of CT (0.522***; effect size=0.32). Drawing on scales from the Activation Lab suite of measures, pre/post surveys revealed increased competency beliefs about computer programming (mean difference =1.13***; effect size=1.01), and increased value assigned to STEM (0.78***; effect size=0.945). We also discuss the design of the instructional sequence and the theoretical framework for its development. 

In order to expand opportunities to learn computer science (CS),there is a growing push for inclusion of CS concepts and practices, such as computational thinking (CT), in required subjects like science. Integrated, transdisciplinary (CS/CT+X) approaches have shown promise for broadening access to CS and CT learning opportunities, addressing potential self-selection bias associated with elective CS coursework and afterschool programs, and promotinga more expansive and authentic contextualization of CS work. Emerging research also points to pedagogical strategies that can transcend simply broadening access, by also working to confront barriers to equitable and inclusive engagement in CS. Yet, approaches to integration vary widely, and there is little consensus on whether and how different models for CS and CT integration contribute to desired outcomes. There has also been little theory development that can ground systematic examination of the affordances and tradeoffs of different models. Toward that end, we propose a typology through which to examine CT integration in science (CT+S). The purpose of delineating a typology of CT+S integration is to encourage instantiation, implementation, and inspection of different models for integration, and to promote shared understanding among learning designers, researchers, and practitioners working at the intersection of CT and science. For each model in the typology, we characterize how CT+S integration is accomplished, the ways in which CT learning supports science learning, and the affordances and tensions for equity and inclusion that may arise upon implementation in science classrooms. 

Computational tools, and the computational thinking (CT) involved in their use, are pervasive in science, supporting and often transforming scientific understanding. Yet, longstanding disparities in access to learning opportunities means that CT’s growing role risks deepening persistent inequities in STEM [2]. To address this problem, our team developed and studied two 10-lesson instructional units for middle school science classrooms, each designed to challenge persistent barriers to equitable participation in STEM. The units aim to position coding as a tool for doing science, and ultimately, encourage a broader range of students, and females in particular, to identify as programmers. Students who participated (n=391) in a recent study of the units demonstrated statistically significant learning gains, as measured on an external assessment of CT. Learning gains were particularly pronounced for female students. Findings suggest that students can develop CT through instruction that foregrounds science, and in ways that lead to more equitable outcomes.