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Background: Despite its obvious relevance to computer science, computational thinking (CT) is transdisciplinary with the potential of impacting one's analytical ability. Although countless efforts have been invested across K‐12 education, there is a paucity of research at the postsecondary level about the extent to which CT can contribute to sustainable learning outcomes. Objectives: The current study examines how a series of Arduino‐based robotics learning activities capture the fuller essence of concepts related to CT. Methods: College students (n = 50) completed a series of six robotics learning activities. Think‐alouds, student reflections and performance scores were used to assess students' CT through a robotics challenge in virtual and physical learning environments. Results and Conclusions: Students verbalized CT concepts related to algorithmic thinking much more than abstraction, problem decomposition and testing and debugging. Students exposed to active learning performed better in a virtual robotics challenge compared to their peers in a traditional‐oriented classroom. Students' scores on the physical robotics challenge increased as a function of the number of references they made to CT concepts during the think‐alouds. It is possible to design pedagogical experiences that tap into various dimensions of CT at incremental levels of complexity through a series of Arduino‐based robotics activities. With the integration of an online simulation, students can visualize and transfer their CT skills between a virtual and physical learning environment, thus leading to more sustainable learning outcomes. Lay Description: What is already known about this topic: Computational thinking (CT) is the conceptual foundation for problem‐solving.CT shifted from unplugged to plugged activities.CT concepts can be fostered through educational robotics. What this paper adds: A multi‐dimensional approach to assessing CT.Applications of CT provide insights into skill transfer.An integrative theoretical approach to study the 'how' and the 'what' of CT. Implications for practice: Educational robotics activities can cater to a fuller range of CT skills.Virtual simulations with constructive feedback are beneficial for students in traditional settings.Mixed learning environments contribute to making links between lectures and labs. [ABSTRACT FROM AUTHOR]

Background: The idea of computational thinking is underpinned by the belief that anyone can learn and use the underlying concepts of computer science to solve everyday problems. However, most studies on the topic have investigated the development of computational thinking through programming activities, which are cognitively demanding. There is a dearth of evidence on how computational thinking augments everyday problem solving when it is decontextualized from programming. Objectives: In this study, we examined how computational thinking, when untangled from the haze of programming, is demonstrated in everyday problem solving, and investigated the features of such solvable problems. Methods: Using a multiple case study approach, we tracked how seven university students used computational thinking to solve the everyday problem of a route planning task as part of an 8‐week‐long Python programming course. Results and Conclusions: Computational thinking practices are latent in everyday problems, and intentionally structuring everyday problems is valuable for discovering the applicability of computational thinking. Decomposition and abstraction are prominent computational thinking components used to simplify everyday problem solving. Implications: It is important to strike a balance between the correctness of algorithms and simplification of the process of everyday problem solving. Lay Description: What is already known about this topic: Computational thinking is a problem solving skill.Computational thinking is different from programming.There is dearth of evidence on how computational thinking augments everyday problem solving. What this paper adds: Computational thinking practices are latent in everyday problem solving.Intentional restructuring of problems is required to uncover how computational thinking applies in solving them. Implications for practices: Decomposition and abstraction are critical in solving everyday problems.Computational thinking should be taught explicitly through everyday problems.Finding balance between the correctness of algorithm and simplification of the problem solving process is important. [ABSTRACT FROM AUTHOR]