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

   Rasul. I., Crabtree (July 2023, Proceedings of the International Conference for the Learning Sciences – ICLS 2012)

   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. Analysing computational thinking in collaborative programming: A quantitative ethnography approach

   https://doi.org/10.1111/jcal.12348  

   Wu, Bian; Hu, Yiling; Ruis, A. R.; Wang, Minhong (February 2019, Journal of Computer Assisted Learning)

   Abstract Computational thinking (CT), the ability to devise computational solutions for real‐life problems, has received growing attention from both educators and researchers. To better improve university students' CT competence, collaborative programming is regarded as an effective learning approach. However, how novice programmers develop CT competence through collaborative problem solving remains unclear. This study adopted an innovative approach, quantitative ethnography, to analyze the collaborative programming activities of a high‐performing and a low‐performing team. Both the discourse analysis and epistemic network models revealed that across concepts, practices, and identity, the high‐performing team exhibited CT that was systematic, whereas the CT of the low‐performing team was characterized by tinkering or guess‐and‐check approaches. However, the low‐performing group's CT development trajectory ultimately converged towards the high‐performing group's. This study thus improves understanding of how novices learn CT, and it illustrates a useful method for modeling CT based in authentic problem‐solving contexts. 

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3. 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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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. Promoting computational thinking through project-based learning

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

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

   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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