Search

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]

Abstract: The long‐standing debate into the potential benefit of developing mathematical thinking skills through learning to program has been reignited with the widespread introduction of programming in schools across many countries, including England where it is a statutory requirement for all pupils to be taught programming from 5 years old. Algorithm is introduced early in the English computing curriculum, yet there is limited knowledge of how young pupils view this concept. This paper explores pupils' (aged 10–11) understandings of algorithm following their engagement with 1 year of ScratchMaths, a curriculum designed to develop computational and mathematical thinking skills through learning to program. A total of 181 pupils from 6 schools undertook a set of written tasks to assess their interpretations and evaluations of different algorithms that solve the same problem, with a subset of these pupils subsequently interviewed to probe their understandings in greater depth. We discuss the different approaches identified, the evaluation criteria they used, and the aspects of the concept that pupils found intuitive or challenging, such as simplification and abstraction. The paper ends with some reflections on the implications of the research, concluding with a set of recommendations for pedagogy in developing primary pupils' algorithmic thinking. [ABSTRACT FROM AUTHOR]

This paper discusses the hierarchical properties of digraphs and proposes flowchart decomposition algorithms using these properties. The hierarchical structure discussed here is based on only the topological relation- skips between vertices and the inclusive relationship among sets of vertices. When the given flowchart represents the control flow of a computer program, this hierarchical structure also shows strong correspondence between these inclusive relationships and program modules. The proposed flowchart decomposition algorithm consists of one algorithm for detecting modules and another algorithm for further decomposing the detected modules. This paper focuses on the former algorithm. Because the hierarchical structure discussed in this paper corresponds to the traditional program modules (e.g., function or procedure) of most of the computer programming languages, the module-detection algorithm can be used not only for decomposing flowchart but for more general purposes, including module design. Two types of module-detection algorithm are discussed in this paper. One detects all modules, and the other detects a restricted subset of modules from the standpoint of flow-chart decomposition. We then demonstrate with a practical example that the latter algorithm is ten times faster than the former. [ABSTRACT FROM AUTHOR]