This article provides an overview of the diverse ways in which computational thinking has been operationalised in the literature. Computational thinking has attracted much interest and debatably ranks in importance with the time-honoured literacy skills of reading, writing, and arithmetic. However, learning interventions in this subject have modelled computational thinking differently. We conducted a systematic review of 81 empirical studies to examine the nature, explicitness, and patterns of definitions of computational thinking. Data analysis revealed that most of the reviewed studies operationalised computational thinking as a composite of programming concepts and preferred definitions from assessment-based frameworks. On the other hand, a substantial number of the studies did not establish the meaning of computational thinking when theorising their interventions nor clearly distinguish between computational thinking and programming. Based on these findings, this article proposes a model of computational thinking that focuses on algorithmic solutions supported by programming concepts which advances the conceptual clarity between computational thinking and programming.

Although abstraction is widely understood to be one of the primary components of computational thinking, the roots of abstraction may be traced back to different fields. Hence, the meaning of abstraction in the context of computational thinking is often confounded, as researchers interpret abstraction through diverse lenses. To disentangle these conceptual threads and gain insight into the operationalisation of abstraction, a systematic review of 96 empirical studies was undertaken. Analysis revealed that identifying features of entities, extracting relevant features, discovering patterns, creating rules and assembling the parts together were the core actions of abstraction. With the primary aim of simplifying practical procedures, abstraction was operationalised as the sophistication of a program, the matching of patterns, the creation of alternative representations, the transfer of solutions, the measurement of a learner’s activity and reading program codes. There is an obvious need for researchers to align the conceptual meanings they have established of abstraction with the practical facts of operationalisation. The need to empirically validate emerging models and the implications for future research are discussed.

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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.

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Most studies suggest that students develop computational thinking (CT) through learning programming. However, when the target of CT is decoupled from programming, emerging evidence challenges the assertion of CT transferability from programming. In this study, CT was operationalized in everyday problem-solving contexts in a learning experiment ( n = 59) that investigated whether learning programming enhances students’ CT skills. Specifically, this study examined the influence of a novel, systematic and micro instructional strategy that is grounded in abstraction and comprised of four independent but related processes – discover, extract, create, and assemble (DECA) towards simplification of problem-solving. Subsidiary questions explored the effects of students’ age, gender, computer proficiency, and prior programming experience on the development of CT. No significant difference was found between the CT skill and programming knowledge of the groups at the posttest. However, within-group paired t-tests showed that the experimental group that integrated DECA had significant improvement in CT but not in the control group across the pretest-posttest axis. Implications of the inconclusive finding about the transfer of programming skills to CT are emphasized and the arguments for disentangling CT from programming are highlighted.