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1. WearableLearning: Developing Computational Thinking through Modeling, Simulation and Computational Problem Solving.

   Rasul. I., Crabtree (July 2023, Proceedings of the 17th International Conference of the Learning Sciences-ICLS 2023. International Society of the Learning Sciences)

   Computational Thinking (CT) is a vital and multi-dimensional skill for all 21st Century Learners. In this study, we investigated the development of three aspects of CT: Self-Perception of Computational Ability, Modeling and Simulation, and Computational Problem Solving, as students engaged in collaborative game design and programming practices. This study contributes evidence for the development of two of these CT dimensions, Modeling and Simulation and Computational Problem Solving, through their engagement with the WL curriculum and platform. We found increases in students’ ability to understand machines and their processes, alongside an improved capacity to think algorithmically as they constructed models, debugged, and iterated through their designs. 

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2. Infusing Computational Thinking into a Computer Science Gateway Course

   Benkarroum, Younes; Azhar, Mohammad Q (April 2024, Journal of computing sciences in colleges)

   Computational thinking (CT) stands as a universal problem-solving approach applicable across diverse disciplines, transcending the domain of computer science. It embodies the mental process of structuring a problem to enable a computational solution feasible for both humans and machines. This methodology involves dissecting problems into smaller parts that are easier to understand and solve. This study delineates a meticulously designed series of CT activities within an introductory computer science course and explores their profound impact on student engagement and problem-solving proficiency. Our findings underscore the pivotal role of hands-on CT practice in augmenting students' ability to decompose problems, recognize patterns, and abstract complexities, and employ algorithms effectively. Notably, this infusion of CT not only cultivates theoretical understanding but also bridges the gap between conceptual knowledge and real-world application through the use of computational tools like Python programming. As CT continues to emerge as a cornerstone skill in diverse domains, this research presents compelling evidence advocating for its integration into introductory courses, laying a robust foundation for students to navigate the evolving technological landscape with enhanced problem-solving capabilities 

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3. Promoting computational thinking through project-based learning

   https://doi.org/10.1186/s43031-021-00033-y  

   Shin, Namsoo; Bowers, Jonathan; Krajcik, Joseph; Damelin, Daniel (December 2021, Disciplinary and Interdisciplinary Science Education Research)

   null (Ed.)

   Abstract This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that  involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students  take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT. 

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4. Promoting Computational Thinking through Project-Based Learning

   https://doi.org/doi.org/10.1186/s43031-021-00033-y  

   Shin, N.; Bowers, J.; Krajcik, J.; Damelin, D. (August 2021, Disciplinary and Interdisciplinary Science Education Research)

   null (Ed.)

   This paper introduces project-based learning (PBL) features for developing technological, curricular, and pedagogical supports to engage students in computational thinking (CT) through modeling. CT is recognized as the collection of approaches that involve people in computational problem solving. CT supports students in deconstructing and reformulating a phenomenon such that it can be resolved using an information-processing agent (human or machine) to reach a scientifically appropriate explanation of a phenomenon. PBL allows students to learn by doing, to apply ideas, figure out how phenomena occur and solve challenging, compelling and complex problems. In doing so, students take part in authentic science practices similar to those of professionals in science or engineering, such as computational thinking. This paper includes 1) CT and its associated aspects, 2) The foundation of PBL, 3) PBL design features to support CT through modeling, and 4) a curriculum example and associated student models to illustrate how particular design features can be used for developing high school physical science materials, such as an evaporative cooling unit to promote the teaching and learning of CT. 

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5. Studying Synergistic Learning of Physics and Computational Thinking in a Learning by Modeling Environment

   Hutchins, N.; Biswas, G.; Conlin, L.; Emara, M.; Grover, S.; Basu, S. (November 2018, Proceedings of the 26th International Conference on Computers in Education. Philippines: Asia-Pacific Society for Computers in Education)

   Synergistic learning of computational thinking (CT) and STEM has proven to effective in helping students develop better understanding of STEM topics, while simultaneously acquiring CT concepts and practices. With the ubiquity of computational devices and tools, advances in technology,and the globalization of product development, it is important for our students to not only develop multi-disciplinary skills acquired through such synergistic learning opportunities, but to also acquire key collaborative learning and problem-solving skills. In this paper, we describe the design and implementation of a collaborative learning-by-modeling environment developed for high school physics classrooms. We develop systematic rubrics and discuss the results of key evaluation schemes to analyze collaborative synergistic learning of physics and CT concepts and practices. 

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