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1. Integrating STEM and Computing in PK-12: Operationalizing Computational Thinking for STEM Learning and Assessment

   https://doi.org/https://doi.dx.org/10.22318/icls2020.1479 https://repository.isls.org//handle/1/6353  

   Grover, S. ; Biswas, G. ; Dickes, A. ; Farris, A. ; Sengupta, P. ; Covitt, B. ; Gunckel, K. ; Berkowitz, A. ; Moore, J. ; Irgens, G. A. ; et al ( June 2020 , The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020)

   Gresalfi, M. ; Horn, I. S. (Ed.)

   There is broad belief that preparing all students in preK-12 for a future in STEM involves integrating computing and computational thinking (CT) tools and practices. Through creating and examining rich “STEM+CT” learning environments that integrate STEM and CT, researchers are defining what CT means in STEM disciplinary settings. This interactive session brings together a diverse spectrum of leading STEM researchers to share how they operationalize CT, what integrated CT and STEM learning looks like in their curriculum, and how this learning is measured. It will serve as a rich opportunity for discussion to help advance the state of the field of STEM and CT integration. 

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2. Computational Thinking in Practice: How STEM Professionals Use CT in Their Work

   Beheshti, E. ( April 2017 , American Education Research Association Annual Meeting 2017)

   The goal of this study is to bring current computational thinking in STEM educational efforts in line with the increasingly computational nature of STEM research practices. We conducted interviews with STEM practitioners in various fields to understand the nature of CT as it happens in authentic research settings and to revisit a first iteration of our definition of CT in form of a taxonomy. This exploration gives us insight into how scientists use computers in their work and help us identify what practices are important to include in high school STEM learning contexts. Our findings will inform the design of classroom activities to better prepare today’s students for the modern STEM landscape that awaits them. 

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3. Integrating Computational Thinking in STEM Education: A Literature Review

   https://doi.org/10.1007/s10763-021-10227-5  

   Wang, Changzhao ; Shen, Ji ; Chao, Jie ( November 2021 , International Journal of Science and Mathematics Education)

   Research focusing on the integration of computational thinking (CT) into science, technology, engineering, and mathematics (STEM) education started to emerge. We conducted a semi-systematic literature review on 55 empirical studies on this topic. Our findings include: (a) the majority of the studies adopted domain-general definitions of CT and a few proposed domain-specific CT definitions in STEM education; (b) the most popular instructional model was problem-based instruction, and the most popular topic contexts included game design, robotics, and computational modelling; (c) while the assessments of student learning in integrated CT and STEM education targeted different objectives with different formats, about a third of them assessed integrated CT and STEM; (d) about a quarter of the studies reported differential learning processes and outcomes between groups, but very few of them investigated how pedagogical design could improve equity. Based on the findings, suggestions for future research and practice in this field are discussed in terms of operationalizing and assessing CT in STEM contexts, instructional strategies for integrating CT in STEM, and research for broadening participation in integrated CT and STEM education. 

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4. Workshops and Co-design Can Help Teachers Integrate Computational Thinking into Their K-12 STEM Classes

   Wu, S. ; Peel, A. ; Bain, C. ; Anton, G. ; Horn, M. ; Wilensky, U. ( April 2020 , Proceedings of International Conference on Computational Thinking Education 2020)

   Kong, S.C. (Ed.)

   This work aims to help high school STEM teachers integrate computational thinking (CT) into their classrooms by engaging teachers as curriculum co-designers. K-12 teachers who are not trained in computer science may not see the value of CT in STEM classrooms and how to engage their students in computational practices that reflect the practices of STEM professionals. To this end, we developed a 4-week professional development workshop for eight science and mathematics high school teachers to co-design computationally enhanced curriculum with our team of researchers. The workshop first provided an introduction to computational practices and tools for STEM education. Then, teachers engaged in co-design to enhance their science and mathematics curricula with computational practices in STEM. Data from surveys and interviews showed that teachers learned about computational thinking, computational tools, coding, and the value of collaboration after the professional development. Further, they were able to integrate multiple computational tools that engage their students in CT-STEM practices. These findings suggest that teachers can learn to use computational practices and tools through workshops, and that teachers collaborating with researchers in co-design to develop computational enhanced STEM curriculum may be a powerful way to engage students and teachers with CT in K-12 classrooms. 

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5. Toward a Theoretical Framework for Data Fluency Teaching and Learning in Middle School STEM

   Wong N. ; Elsayed, R. ; Perez, L. R. ; Daehler, K. R. ; & Chen, P. ( March 2024 , Ninety-seventh annual international conference of the National Association for Research in Science Teaching (NARST))

   This paper presents findings from a qualitative study of eleven experienced STEM educators who worked alongside developers to design and implement data-rich lessons in their grades 6–9 mathematics and science classrooms. In the context of a project that seeks to develop professional learning for data fluency, researchers documented the co-development process to articulate a model of what teachers need to know and be able to do in order to support their students’ data fluency. The project team distilled key findings into two framing documents: 1) a description of high-leverage areas of focus for PL which highlight challenges faced by teachers, which are common, important for data fluency, and represent opportunities for supporting teacher and student growth; and 2) a logic model that describes how the PL course under development is expected to influence teacher, classroom, and student outcomes. This paper contributes to the larger education community by defining the professional learning needs of educators who wish to integrate data into their STEM classrooms. These frameworks provide designers and researchers with touchpoints to structure and study PL experiences, lesson materials, and other classroom resources for both new and veteran educators. These tools can provide STEM teachers with guidance for reflecting on their current knowledge, skills, beliefs, and teaching practices that help their students become more data fluent. 

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