The concept of Computational Thinking (CT) dates back to the 1950s and 1960s (Nicoletti & Suemasu, 2021). Nowadays, however, the term is most frequently associated with Wing´s seminal article on the subject in 2006 (Ezeamuzie & Leung, 2021). Wing (2006) referred to CT as “a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use” (p. 33). She defined it as the “thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out” (Wing, 2017, p. 8). Although there is no universal consensus in the literature on how CT and its key components are to be defined (Fagerlund et al., 2021; Kakavas & Ugolini, 2019), there is general agreement that the practice centers on the concepts involved in problem solving, particularly as applied by computer scientists (Ezeamuzie & Leung, 2021). To this date, training programming skills is considered the most effective means of promoting student CT-related skills (Sun et al., 2021). It is generally recognized that CT is an important cognitive ability which is applicable to problem solving in many areas (Ezeamuzie & Leung, 2021; Hsu et al., 2018), and that it is also linked to an increase in social interactions (Bakala et al., 2021). Consequently, there is now growing global interest in implementing CT in classroom education and in integrating CT into the K-12 curriculum (Bakala et al., 2021; Hsu et al., 2018).

Although CT is considered an essential skill for learners of all ages and should be taught to everyone (Ching et al., 2018), little is known about teaching CT to students with disabilities (Gribble et al., 2020). Due to its beneficial impact on the development of problem-solving abilities and social skills, CT could be particularly valuable for children with autism spectrum disorder (ASD) and/or attention deficit/hyperactivity disorder (ADHD). Both ASD and ADHD are among the most commonly diagnosed neurodevelopmental disorders in childhood. ASD is associated with impaired communication, poor social relationships, and the inability to recognize social cues. Students with ADHD show inattentive symptoms, distractibility and excessive motor activity. This then leads to problems at home, at school, and in other social contexts (Harkins et al., 2021; Salley et al., 2015). As CT entails an interactive, well-structured, step-by-step approach, and fosters self-management in problem solving, CT-skills can be helpful in promoting the inclusion of children diagnosed with ASD or ADHD.

Very few studies have investigated how CT and/or programming may explicitly be used to help students with ASD and/or ADHD or how CT is related to other areas (e.g., the development of social-emotional skills) among such students. In European countries in particular, studies on this matter are scarce. To address this gap in the research, we conducted a scoping review of existing studies in order to make the nature and impact of possible interventions more visible. Our review is based on the following research questions:

1. How are programming skills and/or CT promoted in children (age 6-15) with ASD and/or ADHD?
2. Which other skills may be fostered simultaneously among such children and what is the role of social and emotional skills in such a context?
3. What are the respective challenges and benefits associated with the various interventions?

Since the theoretical and empirical work on this topic is very limited and lacks methodological uniformity, we decided to perform a scoping review. We roughly followed the approach proposed by Arksey & O’Malley (2005) whereby scoping studies are conducted in five (often iteratively rotating) stages: Stage 1: identifying research question(s), Stage 2: identifying relevant studies, Stage 3: study selection, Stage 4: charting, Stage 5: collating, summarizing, reporting. In accordance with our research questions (Stage 1), we determined three sets of search terms, each representing one of three crucial components in our research interest (Stage 2): 1. disorder/disability (ASD and/or ADHD and related symptoms), 2. computational thinking and/or programming, and 3. educational context. These search terms were linked using Boolean operators and, if necessary, the search strings were adapted to the specific requirements of the different databases. The literature search was conducted in two steps in the period from September to December 2020. First, we conducted a search of the relevant publishers' databases in the areas of computer science and education, namely ACM Digital Library, IEEE Xplore, Springer Link, Science Direct, and Taylor and Francis Online. Second, we used Web of Science and Google Scholar to identify any possible additional articles which may have been overlooked in the previous step. Based on our specific research interest, we defined a comprehensive set of inclusion criteria (Stage 3) to determine whether an article was relevant for our research questions. These inclusion criteria reflected the three components of the search strings, and concerned aspects related to methodology and publication-type. The database search yielded 1731 articles that fulfilled the first two inclusion criteria (relevant time period and language). Duplicates were removed and the remaining articles were screened with respect to whether they fulfilled the third and, potentially, also the fourth criterion. This left us with 256 articles. The abstracts of these articles were then checked in more detail to determine whether they complied with criterion 4 and whether the population/sample of the study matched our research interest (criterion 5). At the end of the selection process, 21 articles were identified that fulfilled all five criteria and these were then used in the analysis. The key information/data from the selected papers was then extracted and structured according to the research questions (Stage 4). In a final step, the relevant results were summarized and interpreted (Stage 5).

Our results showed that students with ASD and/or ADHD progressively acquired programming and/or CT skills that persisted beyond the intervention period and point to the beneficial long-term effects of fostering such skills among this group of learners. It also became apparent that the promotion of CT-related skills is particularly effective in enhancing social-emotional competences even when this is not explicitly intended. As students with ASD and ADHD often have difficulties in developing and maintaining social relationships, such an effect is particularly relevant. Although the studies examined rarely addressed the challenges and benefits related to the specific interventions, our results highlight that the following factors are important for success: (a) a planned and well-structured environment, (b) clear and systematic guidance and instruction, (c) individualized support, (d) flexibility in terms of implementation, (e) a positive learning experience involving tasks designed to trigger students´ curiosity, and (f) an opportunity for collaborative learning. We conclude that an approach designed to foster CT and social skills is of particular benefit for children with ASD and/or ADHD. Interventions which focus on both these areas simultaneously, or which foster the development of social-emotional skills through programming are likely to provide a useful contribution to this field of research. To gain more insight into what might work, future studies need to cover the following points: (a) larger sample sizes, (b) properly reporting on sample characteristics, (c) sufficient elaboration of method and study design, (d) using a control group to investigate changes in skills, (e) reporting on follow-up data, (f) clear specification of the intervention goal and its setting, (g) shedding light on inclusive interventions with mixed groups, and (h) equal inclusion of girls and boys while also considering their specific needs.

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