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1. Vectors of CT-ification: Integrating Computational Activities in STEM Classrooms.

   https://doi.org/10.1145/3328778.3372674  

   Bain, C. & (April 2020, Proceedings of tThe 51st ACM Technical Symposium on Computer Science Education)

   null (Ed.)

   While the Next Generation Science Standards set an expectation for developing computer science and computational thinking (CT) practices in the context of science subjects, it is an open question as to how to create curriculum and assessments that develop and measure these practices. In this poster, we show one possible solution to this problem: to introduce students to computer science through infusing computational thinking practices ("CT-ifying") science classrooms. To address this gap, our group has worked to explicitly characterize core CT-STEM practices as specific learning objectives and we use these to guide our development of science curriculum and assessments. However, having these learning objectives in mind is not enough to actually create activities that engage students in CT practices. We have developed along with science teachers, a strategy of examining a teacher’s existing curricula and identifying potential activities and concepts to “CT-ify”, rather than creating entirely new curricula from scratch by using the concept of scale as an “attack vector” to design science units that integrate computational thinking practices into traditional science curricula. We demonstrate how we conceptualize four different versions of scale in science, 1. Time, 2. Size, 3. Number, and 4. Repeatability. We also present examples of these concepts in traditional high school science curricula that hundreds of students in a large urban US school district have used. 

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2. 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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3. An Accessible Computing Curriculum for Students with Autism Spectrum Disorders

   Arslanyilmaz, A. (March 2021, Proceedings of Society for Information Technology & Teacher Education International Conference)

   null (Ed.)

   CT as an essential 21st-century skill and knowledge will be instrumental to new discovery and innovation in all fields of endeavor, and therefore, computing should be taught to all students alongside reading, writing, and arithmetic. However, no computing curriculum has been designed and developed for students with Autism Spectrum Disorders. The objective of this study is to identify and report adaptations and accommodations needed to make an existing computational thinking (CT) curriculum accessible to students with ASD. This objective is accomplished by analyzing sixth-grade students’ characteristics at a school for students with ASD and developing the adaptations and accommodations. The data analyzed and reported for this study consists of systematic documentation of the adaptations and accommodations, including learning objectives, instructional design, information presentation, assessments, feedback, and learning environment. 

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4. A Multiyear Investigation of Student Computational Thinking Concepts, Practices, and Perspectives in an After-School Computing Program

   https://doi.org/10.1177/0735633120905605  

   Mouza, Chrystalla; Pan, Yi-Cheng; Yang, Hui; Pollock, Lori (February 2020, Journal of Educational Computing Research)

   In this work, we examine whether repeated participation in an after-school computing program influenced student learning of computational thinking concepts, practices, and perspectives. We also examine gender differences in learning outcomes. The program was developed through a school–university partnership. Data were collected from 138 students over a 2.5-year period. Data sources included pre–post content assessments of computational concepts related to programming in addition to computational artifacts and interviews with a purposeful sample of 12 participants. Quantitative data were analyzed using statistical methods to identify gains in pre- and post-learning of computational thinking concepts and examine potential gender differences. Interview data were analyzed qualitatively. Results indicated that students made significant gains in their learning of computational thinking concepts and that gains persisted over time. Results also revealed differences in learning of computational thinking concepts among boys and girls both at the beginning and end of the program. Finally, results from student interviews provided insights into the development of computational thinking practices and perspectives over time. Results have implications for the design of after-school computing programs that help broaden participation in computing. 

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5. Coherence across conceptual and computational representations of students’ scientific models.

   Hutchins, N.M.; Basu, S.; McElhaney, K.; Chiu, J.; Fick, S.; Zhang, N.; Biswas, G. (June 2021, Computersupported collaborative learning)

   de Vries, E. (Ed.)

   We articulate a framework for characterizing student learning trajectories as they progress through a scientific modeling curriculum. By maintaining coherence between modeling representations and leveraging key design principles including evidence-centered design, we develop mechanisms to evaluate student science and computational thinking (CT) proficiency as they transition from conceptual to computational modeling representations. We have analyzed pre-post assessments and learning artifacts from 99 6th grade students and present three contrasting vignettes to illustrate students’ learning trajectories as they work on their modeling tasks. Our analysis indicates pathways that support the transition and identify domain-specific support needs. Our findings will inform refinements to our curriculum and scaffolding of students to further support the integrated learning of science and CT. 

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