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1. Integrating Computational Modeling in K-12 STEM Classrooms

   https://doi.org/10.1145/3287324.3293757  

   Biswas, Gautam ; Hutchins, Nicole ; Lédeczi, Ákos ; Grover, Shuchi ; Basu, Satabdi ( February 2019 , Proceedings of the 50th ACM Technical Symposium on Computer Science Education)

   C2STEM is a web-based learning environment founded on a novel paradigm that combines block-structured, visual programming with the concept of domain specific modeling languages (DSMLs) to promote the synergistic learning of discipline-specific and computational thinking (CT) concepts and practices. Our design-based, collaborative learning environment aims to provide students in K-12 classrooms with immersive experiences in CT through computational modeling in realistic scenarios (e.g., building models of scientific phenomena). The goal is to increase student engagement and include inclusive opportunities for developing key computational skills needed for the 21st century workforce. Research implementations that include a semester-long high school physics classroom study have demonstrated the effectiveness of our approach in supporting synergistic learning of STEM and CS/CT concepts and practices, especially when compared to a traditional classroom approach. This technology demonstration will showcase our CS+X (X = physics, marine biology, or earth science) learning environment and associated curricula. Participants can engage in our design process and learn how to develop curricular modules that cover STEM and CS/CT concepts and practices. Our work is supported by an NSF STEM+C grant and involves a multi-institutional team comprising Vanderbilt University, SRI International, Looking Glass Ventures, Stanford University, Salem State University, and ETR. More information, including example computational modeling tasks, can be found at C2STEM.org. 

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2. Orchestrating the Multidisciplinary Implementation of a Narrative-Centered Learning Environment in Upper Elementary Classrooms.

   Vandenberg, J. ; Boulden, D. ; Fox, K. ; Elsayed, R. ; Smith, A. ; Cheuoua, A.H. ; Minogue, J. ; Oliver, K. ; Ringstaff, C. ; Mott, B. ( January 2022 , Proceedings of the International Conference of the Learning Sciences)

   Integration of computational thinking (CT) within STEM subjects is common, although not often at the elementary school level where teachers have minimal experience with CT. We have designed and are refining INFUSECS, a narrative-centered digital learning environment to support upper elementary students’ CT and science knowledge construction as they create digital stories. We used orchestration as our theoretical framework, to examine how elementary teachers planned to approach this multidisciplinary implementation. Through a series of three focus groups, we learned that teachers planned for their students to take notes or utilize other graphic organizers to align the science content with the narrative planning, to engage in collaborative sense-making, and to observe the teacher modeling use of the INFUSECS system. Ultimately, the results have informed the next phase of our research design as we collect teacher and student level data as INFUSECS is utilized in authentic classroom settings. 

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3. Seeds of (r)Evolution: Constructionism Co-Design with High School Science Teachers

   Kelter, J. ; Peel, A. ; Bain, C. ; Anton, G. ; Dabholkar, S. ; Aslan, U. ; Horn, M.S. ; Wilensky, U. ( June 2020 , Constructionism 2020)

   In the decades since Papert published Mindstorms (1980), computation has transformed nearly every branch of scientific practice. Accordingly, there is increasing recognition that computation and computational thinking (CT) must be a core part of STEM education in a broad range of subjects. Previous work has demonstrated the efficacy of incorporating computation into STEM courses and introduced a taxonomy of CT practices in STEM. However, this work rarely involved teachers as more than implementers of units designed by researchers. In The Children’s Machine, Papert asked “What can be done to mobilize the potential force for change inherent in the position of teachers?” (Papert, 1994, pg. 79). We argue that involving teachers as co-design partners supports them to be cultural change agents in education. We report here on the first phase of a research project in which we worked with STEM educators to co-design curricular science units that incorporate computational thinking and practices. Eight high school teachers and one university professor joined nine members of our research team for a month-long Computational Thinking Summer Institute (CTSI). The co-design process was a constructionist design and learning experience for both the teachers and researchers. We focus here on understanding the co-design process and its implications for teachers by asking: (1) How did teachers shift in their attitudes and confidence regarding CT? (2) What different co-design styles emerged and did any tensions arise? Generally, we found that teachers gained confidence and skills in CT and computational tools over the course of the summer. Only one teacher reported a decrease in confidence in one aspect of CT (computational modeling), but this seemed to result from gaining a broader and more nuanced understanding of this rich area. A range of co-design styles emerged over the summer. Some teachers chose to focus on designing the curriculum and advising on the computational tools to be used in it, while leaving the construction of those tools to their co-designers. Other teachers actively participated in constructing models and computational tools themselves. The pluralism of co-design styles allowed teachers of various comfort levels with computation to meaningfully contribute to a computationally enhanced constructionist curriculum. However, it also led to a tension for some teachers between working to finish their curriculum versus gaining experience with computational tools. In the time crunch to complete their unit during CTSI, some teachers chose to save time by working on the curriculum while their co-design partners (researchers) created the supporting computational tools. These teachers still grew in their computational sophistication, but they could not devote as much time as they wanted to their own computational learning. 

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4. A Principled Approach to Designing Assessments That Integrate Science and Computational Thinking

   https://doi.org/10.22318/cscl2018.384  

   Basu, S. ; McElhaney, K. W. ; Grover, S. ; Harris, C. J. ; Biswas, G. ( July 2018 , 13th International Conference of the Learning Sciences (ICLS))

   There is increasing interest in broadening participation in computational thinking (CT) by integrating CT into pre-college STEM curricula and instruction. Science, in particular, is emerging as an important discipline to support integrated learning. This highlights the need for carefully designed assessments targeting the integration of science and CT to help teachers and researchers gauge students’ proficiency with integrating the disciplines. We describe a principled design process to develop assessment tasks and rubrics that integrate concepts and practices across science, CT, and computational modeling. We conducted a pilot study with 10 high school students who responded to integrative assessment tasks as part of a physics-based computational modeling unit. Our findings indicate that the tasks and rubrics successfully elicit both Physics and CT constructs while distinguishing important aspects of proficiency related to the two disciplines. This work illustrates the promise of using such assessments formatively in integrated STEM and computing learning contexts. 

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5. A Systematic Approach for Analyzing Students’ Computational Modeling Processes in C2STEM

   Hutchins, N. ; Biswas, G. ; Grover, S. ; Basu, S. ; & Snyder, C. ( June 2019 , International Conference on Artificial Intelligence in Education (AIED) 2019.)

   Introducing computational modeling into STEM classrooms can provide opportunities for the simultaneous learning of computational thinking (CT) and STEM. This paper describes the C2STEM modeling environment for learning physics, and the processes students can apply to their learning and modeling tasks. We use an unsupervised learning method to characterize student learning behaviors and how these behaviors relate to learning gains in STEM and CT. 

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