General description

Computational thinking (CT) emerged was coined by Perlis in the 1960s. CT was further revised by Papert in the 1980s where he promoted the idea of learning procedural thinking through programming. The concept of CT gained interest by researchers, policy makers and educators after Wing (2006) published her renowned CT article. During the last decade, CT has attracted much attention, and several countries have introduced CT related aims in their curricula. Among the Nordic countries, Finland, Norway, and Sweden have incorporated CT in their curricula while Denmark is piloting aims for learning CT in compulsory education curricula. Although there are several similarities between the education systems in Nordic countries, they have not adopted the same understanding of what is essential in CT. Furthermore, they do not even use the same terms for CT. However, the main understanding of CT in the Nordic countries involves more than programming or coding and includes approaches to problem solving, the role of CT in various school subjects as well as emphasis on transversal competences, such as critical thinking and digital skills (Bocconi, Chioccariello & Earp, 2018).

However, as Bocconi et al. (2018) pointed out, there is a need for shared definitions to accommodate a common understanding of CT even if there are some shared similarities. Although, CT has been broadly discussed in the literature, there is a lack of such understandings. Furthermore, research on CT in mathematics, science, and arts and crafts, or more broadly in science, technology, engineering, arts and mathematics (STEAM) is scarce.

This study is part of the Mathematics, Science and Computational Thinking (MASCOT) project which aims at understanding how CT is integrated in STEAM in primary-, secondary-, and teacher education in Denmark, Finland, and Norway. As part of the project, a framework for what is understood as CT in the beforementioned countries is under development (MASCOT, n.d).

Objectives and research questions

In this study we explore what is the current understanding of CT in STEAM related literature, excluding computer science. Furthermore, we highlight the most prominent areas for further research of CT in STEAM through a descriptive analysis of review articles from January 2000 to October 2021. Our research questions are as follows:

RQ 1: What are the current understandings of what comprises CT identified by review articles in the STEAM-field?

RQ 2: What are the gaps in research on CT identified by review articles after 2000?

Grover and Pea (2013) reviewed the state of the field and found that much of the recent work at the time “focused on definitional issues, and tools that foster CT development” and stating that perhaps the most important questions to be answered were “What, for example, can we expect children to know or do better once they’ve been participating in a curriculum designed to develop CT and how can this be evaluated?” (p. 42).  According to Wing (2006), “computational thinking involves solving problems, designing systems, and understanding human behaviour, by drawing on the concepts fundamental to computer science” (p. 33) and “is a kind of analytical thinking” (Wing, 2008). Later several influential definitions of CT have emerged in the literature (Barr & Stephenson, 2011; Brennan & Resnick, 2012; Weintrop et al., 2016; Shute et al., 2017).

According to Tang et al. (2020), one emergent distinction between various definitions of CT in the literature is between those that focus on CT related to programming and computing concepts (e.g.: Weintrop et al., 2016) and those that focus more on CT as competences needed in both domain specific knowledge and general problem-solving skills (e.g: Shute et al., 2017).

The systematic review followed Fink’s (2019) 7-step process to ensure that the results can be independently reproduced: select research question, identify search terms, screening, pilot review, review, synthesize results, conduct descriptive or analytic review. To answer our research questions and get an overview of the research field, we ran database searches and conducted a screening to identify review articles of interest. The most important inclusion criteria were computational thinking and programming. Additionally, boolean operators were used in the search string to limit the search to the planned education levels and school subjects, and to include synonyms of the key words. The most important exclusion criteria were subjects that were not part of STEAM, such as computer science, articles that were not reviews, special education and non-compulsory education. The search was limited to peer reviewed journal articles published in English between January 1st 2000 and October 13th 2021. The search was run in selected databases: EBSCOHost, Scopus, JSTOR, and WebOfScience. After removing duplicates in EndNote, 544 articles were imported to Rayyan where three researchers screened each abstract. To ensure inter-rater reliability, two of the researchers screened 272 abstracts each, while the other 8 researchers screened 136 each. After the first screening, 13 articles were included based on the inclusion and exclusion criteria. After the screening, the 13 chosen review articles were each coded by two coders. The codes were decided on in advance based on the research questions. The results were cross-checked and discussed between coders. The findings were then synthesized in a document, and further analyzed according to the research questions.

The 13 included articles used a wide variety of CT definitions, often referencing elements from various definitions and seldom concurrent. The most referenced definitions were those of Wing (2006; 2008; 2011), with seven articles in total, Grover & Pea (2012; 2013; 2018) were referenced by five, articles and Brennan & Resnick (2012) by four. The identified gaps in research on CT within the STEAM-field broadly fall into three categories: research methods, settings and instruments; learning environments, coding tools, learning process and learning outcomes; and assessment. Grover & Pea (2013) highlight the necessity for more empirical studies that also report reliability and validity evidence (Tang et al., 2020). Also lacking is more research in settings of informal learning such as Fab Labs and Makerspaces (Grover & Pea, 2013; Tang et al., 2020), teacher-training (Cutumisu, 2019; Tang et al., 2020), primary education (Kakavas & Ugolini, 2019) and high school (Tang et al. 2020). The gaps in research on student outcomes are related to designing learning progressions (Kakavas & Ugolini, 2019; Popat & Starkey, 2019), student learning (Grover & Pea, 2013), quality of understanding attained (Fagerlund et al., 2021) and transferability of CT skills to other subject areas or skills (Popat & Starkey, 2019; Tang et al. 2020), particularly transversal competencies (Grover & Pea, 2013; Kakavas & Ugolini, 2019). Kakavas and Ugolini (2019) call for more research on non-programming related CT-activities in cultivating CT-skills as well as using text-based programming languages. There is a need for developing reliable and valid universal assessment tools (Cutumisu, 2019; Kakavas & Ugolini, 2019; Tang et al., 2020). These tools are needed for all CT concepts, practices and perspectives, including assessment of higher order thinking skills (Cutumisu, 2019), and should make use of concurrent qualitative measures such as interviews and think-aloud groups (Tang et al., 2020).

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