A Bibliometric Review Leveraging the use of Computational Thinking for educational sustainability in STEM subjects: A Framework for Teaching and Learning in the 21st Century.

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A Bibliometric Review Leveraging the use of Computational Thinking for educational sustainability in STEM subjects: A Framework for Teaching and Learning in the 21^st Century.

https://doi.org/10.21203/rs.3.rs-4832810/v1

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Computational Thinking (CT) has been recognised as an innovation framework to solve abstract problems to manageable tasks. In manufacturing industry, it plays a significant role to develop some working systems and application. Similarly, in teaching and learning, educators may apply this technique to unpack a rather challenging problem to solve into a more granularly structured concept that is easier to understand. This study uses a bibliometric method that build firm foundations on how school educators can integrate CT in their classroom instruction to advance some novel and meaningful ways of teaching. The data used was gathered from the Scopus database. Keywords such as “computational thinking,” “educational sustainability” and “teaching and learning” were used to extract papers from the database. Boolean operators such as ‘AND’ and ‘OR’ were used as wildcards to assist the searching of journals and conference proceedings in the related field. VOSviewer was used to prepare a co-authorship and co-occurrence map based on the bibliographic data gathered. The findings from the review of the literature indicates a huge knowledge gap in teaching using CT approach compared to a ‘common approach’ or ‘traditional’ way of teaching. Although CT approach is often used in computer science and related fields, it can also be used in other subjects, particularly, STEM subjects. The limitation of the study is that only one database, Scopus, was used to gather data. Despite the use of a single database, the findings open a novel knowledge gap that can be explored to enhance teaching and learning approach.

Educational Philosophy and Theory

Computational Thinking

educational sustainability

teaching and learning

21st century skills

framework

The acquisition of computational thinking skills is paramount for the comprehension and adept engagement with forthcoming technologies. Rather than mere proficiency in a specific array of knowledge concerning a singular device or programming language, it embodies a cognitive methodology. Although computational thinking (CT) is frequently conjoined with computers and coding, it is imperative to acknowledge its potential impartation even in the absence of tangible computational devices (Asunda, Faezipour, Tolemy & Do Engel, 2023). Teaching using CT approach helps scaffold learners to understand the problem at hand, by effectively breaking it down into more manageable steps, thereby enhancing their comprehension and mastery of the subject (Lee, Wu, Lin, Wang & Huang, 2024; Mumcu, Uslu, & Yıldız, 2023). As technological advancements progress and assume a more prominent role in societal frameworks, computational thinking (CT), defined as the aptitude to apprehend computational concepts and processes and their pertinence in the tools we employ, emerges as a pivotal facet of contemporary educational paradigms (Suh & An, 2022). The realm of education has undergone considerable transformations in pedagogical approaches and learning methodologies, catalysed by substantial technological innovations in response to Artificial Intelligence (AI) and related emergent technologies. Artificial intelligence (AI) technology is seen as the newest disruption impacting ethical, societal, economic, and educational norms, particularly in professional spheres. It is increasingly discussed within healthcare and education, with expectations of enhancing decision-making, education quality and service delivery (Connolly, Hernon, Carr, Worlikar, McCabe, Doran ... & O’Keeffe, 2023). The integration of AI in education, emphasizes the critical need for automatic detection of individual learning styles (Ezzaim, Dahbi, Aqqal & Haidine, 2024). Traditional methods such as tests or questionnaires, though reliable, face challenges including student reluctance and limited self-awareness of learning preferences (Ezzaim et al., 2024). This underscores a research gap in learning style detection within adaptive and self-directed learning, necessitating further investigation into the effectiveness of CT for automatic detection within AI-driven systems in real-world educational settings. Different learners have learning styles such as the cognitive, affective, and physiological behaviours that students use to begin concentrating on, processing, assimilating, and remembering new and challenging learning material as well as perceiving, interacting with, and responding to the learning environment (Ezzaim et al., 2024). The significance of identifying learning styles within the context of STEM subjects lies in the potential to personalize and optimize the learning experience for individual learners (Waladi et al., 2023) through use of CT. By accurately identifying a learner’s unique learning style, CT can dynamically tailor content and instructional strategies to align with the learner’s preferences, strengths, and needs (Benfarha & Lamarti, 2023).

CT in connection with AI tools has garnered acknowledgment as an indispensable skillset for problem-solving across diverse disciplines. Consequently, priming learners to cogitate computationally while tackling multifaceted challenges across various subjects ought to be initiated at early developmental stages and recurrently reinforced throughout their formative educational trajectory. Nevertheless, this endeavor faces impediments as many educators and learners exhibit a heterogeneous background in computational skills, hindering the seamless implementation of computational thinking methodologies. There exists a burgeoning apprehension regarding how educational institutions can effectively steer students and educators in leveraging the technological capabilities inherent in the Fourth Industrial Revolution (Ogegbo & Ramnarain, 2023).

Utilizing Computational Thinking (CT) to foster educational sustainability within the realm of Science, Technology, Engineering, and Mathematics (STEM) subjects emerges as a strategic endeavour aimed at augmenting learning outcomes and equipping students with requisite competencies to confront the challenges of the 21st century. The integration of STEM disciplines into educational curricula assumes paramount importance, serving as a conduit for endowing students with a multifaceted skill set and aptitudes from an early developmental stage. These proficiencies hold not only local significance but also wield global applicability, transcending both professional and academic domains (Paucar-Curasma, Cerna-Ruiz, Acra-Despradel, Villalba-Condori, Massa-Palacios, Olivera-Chura & Esteban-Robladillo, 2023). Such an approach engenders a sense of aspiration, stimulating individuals to contemplate careers across diverse and in-demand STEM sectors, both in the present and the foreseeable future.Computational Thinking (CT), rooted in problem-solving methodologies drawn from computer science principles, emerges as a pivotal pedagogical tool applicable across various domains, notably within STEM education. CT has garnered recognition as an effective strategy for navigating abstract and intricate challenges inherent in STEM subjects. However, its adoption remains limited, particularly in developing nations like South Africa, where conventional teaching methodologies persist as the predominant mode of instruction. CT-based pedagogy espouses a constructivist framework, proving instrumental in facilitating students' comprehension of STEM and CT concepts while concurrently honing their problem-solving acumen (Snyder, Hutchins, Biswas, Narasimham, Emara, & Yett, 2022). According to Emara, Hutchins, Grover, Snyder, and Biswas (2021), CT engenders the cultivation of robust, collaboratively nurtured problem-solving proficiencies. Current teaching paradigms continue to accentuate pedagogical philosophies such as constructivism, situated learning, pragmatism, and critical realism, all of which have undergone transformative influences in light of technological advancements permeating this epoch. It is imperative for students to possess foundational CT knowledge and adeptness in leveraging intricate synergistic processes, such as transmuting historical data into computational formats and articulating these methodologies to fellow peers, particularly within the context of STEM instruction. Such a milieu presents educators with a unique opportunity to engender knowledge creation among STEM students, fostering collaborative problem-solving endeavors through the prism of CT within the classroom setting (Snyder et al., 2022).

On a global scale, there exists an enduring trend that involves the integration of computer science and programming into education. This integration is driven by the current and future demands of the job market, as well as the belief that computer science and programming skills are essential for individuals to thrive in an increasingly digitalized society (Humble & Mozelius). The concept of computational thinking (CT) serves as a comprehensive framework for encapsulating the abilities associated with programming and computer science. It has gained widespread recognition in both research and education. However, this study seeks to answer the question: What are the most effective ways for STEM educators to use computational thinking (CT) to improve students' abstract problem-solving skills and promote educational sustainability?

In the era of the 21^st century, Computational thinking (CT) is one of the pedagogical approaches that STEM educators should practice in their classrooms. As demonstrated in Tikva and Tambouris' study (2021), CT, particularly through programming, garners heightened interest as an optimal avenue for fostering 21st-century skills. Notable examples of these skills include critical thinking, creativity, and collaboration (Tzagkaraki, Papadakis & Kalogiannakis, 2021). CT is becoming more popular in schools throughout the world (Lye & Koh, 2014). CT can be applied to all educational disciplines, if the educators understand computing concepts. Wing (2006) assert that CT is a process that should be followed when solving a particular problem. In includes identifying the problem, designing, and solving the problem, using the fundamental concepts of computer science. The original characterization of CT by Wing (2006) equated it with thinking in the manner of a computer scientist, has encountered criticism on multiple occasions. The crux of the critique revolves around the idea that CT holds a wealth of intriguing applications for addressing practical real-world issues that transcend the domain of computer science. From this assertion, CT can refer to the mental processes required in developing algorithmic, or step-by-step, solutions that a computer can execute (Wing, 2006). Aho (2012) considers CT as “the thought processes involved in formulating problems so their solutions can be represented as computational steps and algorithms” (p.832). CT includes being able to comprehend, analyze, and decompose a problem into smaller problems, identify patterns in the problems, abstract the problems by focusing on the essence of the problems and ignoring details at first, design a step-by-step process to solve the problem, and test and debug the solution (Ching and Hsu, 2023). According to the Computer Science Teachers Association (CSTA) (2011), CT is the process of formulating problems, organizing, and analysing data, representing data through abstractions, automating solutions through algorithmic thinking, implementing potential solutions, and generalizing and transferring this problem-solving process.

CT is the conceptual foundation required for defining and solve real-world issues utilizing algorithmic approaches that are can be transferred and essential to many settings and disciplines (Shute, Sun, & Asbell-Clarke, 2017). It is a skill that helps in the development of thinking abilities and gives methods for extracting knowledge hidden in data (Buitrago Flórez et al., 2017). CT is an alternate method to abstract problem-solving by developing abilities created in its framework; by applying computational technologies, individuals comprehend a crucial way of thinking in the digital era. Wing (2006) proposed this way of thinking and highlighted its five components: problem reformulation, recursion, problem decomposition, abstraction, and systematic testing to accomplish problem-solving. In this context, CT enables individuals to develop sub-competences of reasoning for complexity by focusing on the development of creative, critical, and systemic thinking (Doleck, Bazelais, Lemay, Saxena, & Basnet, 2017; Kules, 2016) together with scientific thinking (Wing, 2008). This method enables students to construct answers while enhancing their systemic thinking to address challenging situations. Complex and computational thinking aim to cultivate specific abilities and sub-competencies in individuals, facilitating their success in demanding scenarios. According to the findings of Humble & Mozelius (2023), teachers recognize various opportunities and challenges in integrating the concept of CT into their teaching practices. They acknowledge that CT can improve the clarity of concepts through the introduction of new teaching methods while reinforcing traditional ones. However, concerns exist among some teachers who perceive CT as overly intricate and challenging for certain students. A study conducted by Miller‐Rushing & Brasili (2024) revealed that teachers need support and some training that will instil confidence and perceived ability on how to incorporate CT in various subjects with the STEM disciplines to provide and support to the learners de the scaffold. Moreover, a substantial number of teachers view CT as relevant not only to mathematics and technology but also to broader learning contexts. In essence, CT has the potential to enhance teaching and learning in mathematics, technology, and other STEM subjects, provided it is approached with depth rather than abstractly or superficially. The integration of CT into STEM education offers numerous benefits that contribute to educational sustainability, fostering long-term growth and engagement in these crucial disciplines. By imparting CT skills to learners, a versatile problem-solving mindset is developed, extending beyond the classroom, and enhancing their capacity to adapt, innovate, and excel in an increasingly technology-driven world.

Alfaro-Ponce, Patiño & Sanabria (2023) provide six core components of CT that can be used for educational sustainability in STEM subjects that may help students to develop the necessary skills for problem-solving in complex environments. These components build a student-centred learning environment focusing on problem solving and guided learning with student self-awareness (Li, 2020). Basing on the literature, the following components were identified in the definition of CT: problem solving, decomposition, abstraction, algorithmic thinking, debugging, Iteration, and generalization (Adler et al. 2023; Humble & Mozelius, 2023 and Ching and Hsu, 2023). Table 1 provides the summary of CT components, relation, and their definitions.

Table 1: Summary of CT facets and their definitions - Source: Shute, Sun & Asbell-Clarke (2017)

Facet

Definition

Decomposition

Dissect a complex problem/system into manageable parts. The divided parts are not random pieces, but functional elements that collectively comprise the whole system/problem.

Abstraction

Extract the essence of a (complex) system. Abstraction has three sub-categories:

Data collection and analysis: Collect the most relevant and important information from multiple sources and understand.

Pattern recognition: Identify patterns/rules underlying the data/information structure.

Modelling: Build models or simulations to represent how a system operates and/or how a system will function in future

Algorithms

Design logical and ordered instructions for rendering a solution to a problem. The instructions can be carried out by a human or computer. There are four sub-categories:

Algorithm design: Create a series of ordered steps to solve a problem.

Parallelism: Carry out a certain number of steps at the same time.

Efficiency: Design the fewest number of steps to solve a problem, removing redundant and unnecessary steps.

Automation: Automate the execution of the procedure when required to solve similar problems.

Debugging

Detect and identify errors, and then fix the errors,

when a solution does not work as it should.

Iteration

Repeat design processes to refine solutions, until the

ideal result is achieved.

Generalization

Transfer CT skills to a wide range of situations/domains to solve problems effectively and

efficiently.

Decomposition guarantees that intricate problems can be broken down into smaller, meticulously identifiable parts, enabling a focused approach to addressing the core components. Wing's (2006) initial unveiling of the CT concept included the concept of employing decomposition to simplify large and complex tasks into smaller, manageable units. One could make the case that this concept has significantly earlier origins, rooted in the principle of "divide and conquer" frequently taught in artificial intelligence and computer science. This aspect typically fosters systemic and critical thinking. Teachers should recognise decomposition as associated with general problem-solving and can serve as an educational tool for helping students comprehend instructions. It is used commonly among educators to break down subject matter for students. Thus, this approach of instructions can be viewed as a distinct link between CT decomposition and how teachers divide complex problems into more manageable components within their respective subjects.

Abstraction serves as a complementary approach to decomposition in addressing extensive and intricate issues. Wing's highly optimistic perspective on abstraction describes it as having the confidence to work with, adapt, and impact a complex system without necessitating an exhaustive understanding of every detail. Abstraction simplifies the process of identifying the fundamental components within a complex problem. According to Shute et al. (2017), this concept comprises three subcategories: data collection and analysis, pattern recognition, and modelling. Letting student practice this method of problem solving, they are developing innovative and systematic thinking.

Algorithm is commonly broadly defined and sometimes resembles heuristic recipes (Alfaro-Ponce, Patiño & Sanabria-Z, 2023). However, it should ideally consist of a finite sequence of precisely defined instructions designed to solve a specific problem or a category of problems. Algorithms play a crucial role in delineating the logical and sequential steps required to solve particular problems or tasks. This concept encompasses four subcategories: algorithm design, parallelism, efficiency, and automation (Shute et al., 2017). This part of problem thinking plays a bigger role in increasing learner cognitive processes as it imparts the scientific and critical thinking.

Debugging is primarily centred on mitigating damage and addressing errors, involving testing, and debugging procedures. Its main objective is to identify and rectify issues within a process, which aids learners in cultivating critical and creative thinking skills. CT debugging is seen as a means to cultivate patience, promote collaboration and discussions, and align with the practice of guiding students. Collaborative learning involves an instructional approach where students, regardless of their performance levels, collaborate in small groups to achieve a shared objective (Xu, Wang & Wang, 2023; Gokhale, 1995).Teachers need to emphasise that debugging can serve as a method to develop students' patience while performing calculations or searching for errors recipes (Alfaro-Ponce, Patiño & Sanabria-Z, 2023).

Iteration is embraces as a means of instilling computational thinking in classrooms is a crucial element in the endeavours to empower students as creators of tools rather than just consumers. Some might argue that, on one hand, iteration shares similarities with relatively fundamental programming techniques and possesses a structure no more intricate than selection (Humble & Mozelius, 2023). Iteration involves repeating various tasks within the process as many times as needed to enhance them. The logic around iteration is to repeat the design processes iteratively to improve solutions until the desired outcome is reached. This practice equips learners with scientific thinking skills needed in the era of the 21^st century. For example, this component could be used in mathematics in solving Fibonacci series, searching, and traversing data structures in computer science, Newton-Raphson Method in solving non-square and non-linear problems, with the idea of iteratively finding improvingly better approximations (Akram and Ann, 2015).

Generalization can be understood as the ability to apply knowledge and skills from one domain to various other domains. The underlying concept is that individuals who have previously addressed similar but less complex problems are better equipped to tackle more intricate issues (Shute et al., 2017). The goal is to transfer computational thinking skills across a broad spectrum of situations and domains, enabling effective and efficient problem-solving. Generalization seeks to recognize trends and patterns in the problem, relationships, forms of interaction, and potential solutions that can be applied to new situations. The methodical and systematic approach inherent in CT can be extended to multiple domains, particularly within other STEM subjects, fostering the development of scientific and systemic thinking skills (Alfaro-Ponce, Patiño & Sanabria-Z, 2023).

Given the pervasive presence of digital technology in today's society, the integration of CT into education is not limited to the field of computing alone (Adler, Hibdon, Kim, Mayle, Pines, and Srinivas, 2023). A study conducted by Adler et al. (2023) demonstrates that CT is effective for enhancing math and science education, highlighting it as a crucial analytical skill that should be nurtured from a young age (Ching and Hsu, 2023). To foster a profound understanding of CT, there is a necessity to introduce Computer Science as a subject in the curriculum, aiding in the comprehension of CT concepts. Effective integration of CT into STEM education for sustainability requires educators to receive training in CT methodologies and tools. Furthermore, curriculum development should prioritize the infusion of CT principles into STEM courses and projects. By emphasizing real-world applications, such as sustainability challenges, STEM subjects can become more engaging and relevant to students, thereby promoting educational sustainability. The research carried out by Adler, Hibdon, Kim, Mayle, Pines, and Srinivas (2023) revealed that students achieved impressive scores upon completing the module that incorporated computational thinking (CT), with over 90% demonstrating proficiency or high proficiency in all the concepts they had learned. The study also observed enhancements in assignments that were tackled through the application of CT. Moreover, students' self-confidence in each CT aspect showed significant improvement from the start to the conclusion of the teaching term..

This study used bibliometric review to analyse and synthesise some information, focusing on findings and not simply bibliographic citation but summarising the substance of the literature and drawing conclusion from it. Bibliometrics is a method that includes statistical analysis of published articles and citations therein to measure their impact. Fetscherin & Usunier (2012) view Bibliometric analysis as “unveils pivotal articles and objectively illustrates the linkages between and among articles about a certain research topic or fields by analysing how many times they have been co-cited by other published articles” (p. 735). The aim was to determine how STEM educators use CT to enhance teaching of abstract concepts to more concrete, using critical thinking skills that facilitate educational sustainability. An educator who employs excessive abstraction in teaching and learning activities runs the risk of confusing students. A descriptive quantitative analysis of the concept of CT for sustainable in STEM subjects was analysed from 2018 to present (2023). Scopus database was used because many researchers in humanity publish on this site than in Web of Science database. It has a collection of a wide range of bibliographic databases, citations, and references of scientific publications in any discipline of knowledge. To collect comprehensive data, a two-step approach was used. First, the title of the paper had to include one of the following keyword combinations: “Computational Thinking” AND “educational sustainability” AND “STEM subjects” as well as one of the following terms: “Teaching and Learning” OR “leveraging” OR “21^st Century”. The ‘AND’ and’ OR’ are Boolean operators used to search and return for some information. The use of these terms considered to gather relevant data that define the world guided by CT to leverage educational sustainability in the 21^st century. Twenty-three (23) documents were found and were exported to CSV file and later imported to MS Excel. The Scopus bibliometric data was imported into VosViewer. VOSviewer is a powerful tool used to analyse the structure of scientific field. The tool can be used to analyse and visualise different types of bibliometric networks. In this study, VOSViewer is used to analyse the own dataset and to create the researcher own visualisation.

Scopus and Web of Science Databases are used for bibliometric or systematic literature reviews These two databases require institutional access and many people all over the world may not have access to these two databases. On VOS Viewer, I created a network map using bibliographic data. Three options appeared to create a map based on network data. We could create a map based on

Bibliographic data
Text Data

A bibliographic data was used. Once selected, it gives the options to choose the files from the databases. Scopus tab was selected to extract some data for bibliometric analysis. Figure 1 shows the bibliometric analysis and citation mapping process was used.

Bibliometric results, Visualisation and Analysis

The document source publications

Table 2 displays the top twenty (20) primary journals that have published CT and framework research with at least one article. The Journal of Education and Information Technologies has contributed 83 articles (with 295 citations), while Computers and Education has featured 28 articles (accumulating 984 citations). These two journals stand out as the leaders in CT research publication, indicating their significant authority and popularity within this field, as well as their recognition by researchers. This also highlights the open-access publishing opportunity for CT pedagogical instruction research. Of importance to note are conference proceedings that discuss issues around teaching STEM using CT approach. ACM International Conference Proceeding Series has held 127 conferences with 143 citations more than other conferences. It is worth noting that no South African journals made it onto the list, which implies that South Africa and several other African countries have yet to fully embrace digital technologies in their educational practices.

Table 2: Number of publications per source

Publication Source	Documents Records:2018 T0 2023	Citations: :2018 T0 2023
ACM International Conference Proceeding Series	127	143
Education And Information Technologies	83	295
Annual Conference on Innovation and Technology in Computer Science Education, ITICSE	48	37
Journal Of Educational Computing Research	48	526
Proceedings - Frontiers in Education Conference, Fie	45	90
SIGCSE 2020 - Proceedings of the 51st ACM Technical Symposium on Computer Science Education	39	248
Interactive Learning Environments	30	367
CEUR Workshop Proceedings	29	18
IEEE Global Engineering Education Conference, EDUCON	29	110
Computers And Education	28	984
Journal Of Science Education and Technology	27	588
Since 2023 - Proceedings Of The 54th ACM Technical Symposium on Computer Science Education	27	6
Computer Applications in Engineering Education	26	272
Education Sciences	25	91
Computer-Supported Collaborative Learning Conference, CSCL	24	41
Sustainability (Switzerland)	24	64
Informatics In Education	23	170
SIGCSE 2021 - Proceedings of the 52nd ACM Technical Symposium on Computer Science Education	23	124
Computer Science Education	22	106
Educational Technology Research and Development	22	170
Proceedings Of International Conference on Computational Thinking Education	22	28

Analysis of co-authorship per country

Educators operating within the educational information and technology policy and framework domain, in concert with fellow researchers, avail themselves of opportunities to fortify their pedagogical strategies through collaborative endeavours aimed at disseminating peer-reviewed publications. These publications serve to enlighten the educational community on effective methodologies for integrating computational thinking into instruction, thereby fostering enhanced problem-solving approaches within STEM subjects. The dissemination of research findings via peer-reviewed journals, conference proceedings, and community of practice platforms within the computing sphere affords the educational fraternity the chance to evaluate the efficacy of computational thinking in elucidating abstract concepts, thereby rendering them more comprehensible to STEM learners. Within these interactive forums, a reciprocal exchange of skills, knowledge, and techniques transpires, yielding mutual benefits for all stakeholders and equipping them with proficiencies encompassing scientific, critical, creative, and systemic thinking.

Analysis of the research landscape concerning Computational Thinking (CT) unveils a notable trend wherein the majority of authors exhibit a limited publication record, typically not exceeding two publications. These authors are subsequently stratified into distinct clusters predicated on their co-authorship relations, as delineated in Figure 2. Significantly, these authors boast affiliations with institutions and organizations spanning the United States, China, Taiwan, and Canada, indicative of a heterogeneous and globally dispersed collaboration within the CT research community. This transcontinental distribution underscores a pervasive engagement and collaborative ethos among researchers dedicated to advancing the comprehension and application of CT principles. The constrained publication output per author may signify a propensity toward specialization or focused contributions within the CT domain, underscoring the decentralized nature of expertise and knowledge propagation within this dynamic realm of inquiry.

Using the VosViewer, an analysis of co-authorship among scholars specializing in Computational Thinking (CT) was conducted on a global scale. The criterion for inclusion required a minimum of 5 publications per country, resulting in 49 out of the 115 countries meeting this threshold and engaging in active collaboration. The findings presented in Table 3 highlight the nations demonstrating the highest levels of collaboration and research output, namely the USA, China, Spain, Taiwan, Turkey, Hong Kong, Barazi, and Malaysia. Notably, the analysis does not indicate active collaboration between South Africa and these prolific countries.

Analysis of co-occurrence of keywords

Keyword research in the context of Computational Thinking (CT) investigation not only signifies the focal points and trajectory of the domain but also plays a crucial role in categorizing essential themes within CT and sustainable STEM education. An examination of keyword occurrences within the framework of CT research unveils the prevailing topics in the field during the recent time span. The study established a default minimum of five (5) keywords for extraction, a choice aligned with the extensive data utilized and consistent with prior research methodologies (Aghimien, Aigbavboa, Oke, & Thwala, 2020). Among the 2400 keywords within the dataset, 212 met the stipulated threshold for further scrutiny, following the exclusion of generic terms, country names, and geographical regions. Subsequently, 197 keywords were subjected to the ultimate analysis, classified into eight clusters. In accordance with Van Eck and Waltman (2014), the proximity of keywords on the network visualization map (Figure 3) denotes their frequency, with larger node sizes indicating a concentrated occurrence. Additionally, robust association links between keywords, as elucidated by Li et al. (2020), signify a strong correlation among two or more keywords.

In Figure 3, a comprehensive analysis of keyword frequency unveils several prominent terms in the realm of Computational Thinking (CT) research. Notably, 'Computational Thinking' appears most frequently, occurring 611 times, followed by 'Computational Thinking Education' (359 times), 'STEM Education' (313 times), 'Educational Sustainability' (87 times), 'Teaching and learning' (211 times), '21st Century' (139 times) and 'Framework' (93 times). These keywords, along with others, are systematically grouped into clusters. The insights derived from a meticulous review of the network and density visualization maps are instrumental in categorizing the underlying semantic themes within the textual dataset of keywords. As highlighted by Olawumi and Chan (2018), the critical examination of cluster analysis allows researchers to effectively categorize a substantial volume of research data into distinct groups, facilitating the identification of research themes, trends, and their interconnections. Three clusters were strongly connected to the intellectual structure of CT framework research after a critical review.

Cluster 1: Computational Thinking

Cluster 1 is comprising 38 keywords with occurrences of five or more, demonstrates a substantial connection to the central concept of Computational Thinking (CT), as illustrated in Figure 3. Key words within this cluster include 'Computational Thinking,' 'algorithms,' 'decomposition,' 'Debugging,' 'Abstraction,' 'iteration,' 'generalization,' and 'pattern recognition.' These keywords collectively reinforce the notion that the fundamental objective of employing CT is to facilitate problem-solving. The cluster's emphasis on these specific terms underscores that CT serves as a fundamental tool for empowering the younger generation with the essential skills required for future economic growth. It highlights the importance of concepts such as algorithms, decomposition, debugging, abstraction, iteration, generalization, and pattern recognition in developing problem-solving abilities and preparing individuals for the challenges of the evolving economic landscape.

Cluster 2: Integrating CT for Sustainable STEM Education

Cluster 2 comprised of 28 co-occurring keywords strongly linked to the main keywords in Figure 3, sheds light on additional crucial aspects of Computational Thinking (CT) integration. The keywords within this cluster include 'Technological Integration,' 'Computer Science,' 'Mathematics,' 'Science,' 'Engineering,' '21st Classrooms,' 'sustainability,' and 'equal access to devices.' This grouping emphasizes the necessity for every school to adopt 21st-century classrooms conducive to teaching through computational thinking. It underscores the importance of policies that ensure the availability of resources before the commencement of teaching and learning activities. Such proactive measures facilitate the ease of incorporating CT pedagogy for effective problem-solving. Furthermore, the cluster emphasizes the significance of introducing learners to computer science courses, including programming, as a means to equip them with the necessary skills for problem-solving using CT. Programming offers a valuable avenue for enhancing students' CT proficiency. Through the process of creating instructions using a codified language (i.e., programming language), students gain the skills to instruct computers to perform specific tasks or address problems (Wang, Shen, & Chao, 2022). This holistic approach ensures that both the learning environment and educational policies align with the principles of CT, promoting comprehensive and effective integration into the educational system.

Cluster 3: Challenges STEM educators face in integrating CT.

The cluster comprised 17 keywords, such as ‘digital tools,' 'programming,' 'computational thinking,' 'skills,' 'framework,' 'connectivity issues,' 'digital skills,' 'support,' and 'artificial intelligence.' Computational thinking has traditionally been prominent in computer science, but its recent application extends to various fields like engineering, medicine, and electronics. In the realm of education, there is no specific policy mandating the use of computational thinking in teaching and learning. Educators are granted the autonomy to employ this approach for problem-solving. Dong, Cateté, Lytle, Isvik, Barnes, Jocius, ... & Andrews, A, (2019), given that most teachers lack previous training in computer science or the practices and skills associated with computational thinking [10], it is essential to provide them with adequate training to enable the design and delivery of curricula enriched with computational thinking. In other words, not all STEM educators possess the requisite knowledge for implementing this pedagogical method. While adopting a CT approach in STEM education does not serve as a universal solution to learners’ challenges in problem-solving skills, it is recognized as a beneficial method that empowers learners to break down intricate problems into more manageable steps. This skill is deemed crucial for addressing complex STEM problems, guiding learners to systematically analyze issues, recognize patterns, and formulate creative solutions.

In 22 reviewed studies, a variety of computational thinking assessment methods were identified. Most studies focused on well-defined concepts and practices aligned with Brennan and Resnick's framework (2012) for understanding and teaching computational thinking (CT). The results showed that students that are good in programming also do well problem-solving skills and working collaboratively. The International Society for Technology in Education (ISTE) and the Computer Science Teachers Association (CSTA) (2011) provide some research that show broader instances for problem solving using the principles and definition of CT. It has helped to clarify the meaning of CT and to provide a common framework for teaching and learning CT. Addressing this gap in the literature is essential, as it is crucial to establish valid and reliable assessment methods for measuring computational thinking development, advancing research in this field. The research conducted by Waterman, Goldsmith, and Pasquale (2020) suggests that the endeavour to incorporate computational thinking into elementary core subjects show promise. The study revealed that learners exhibited ideas that showcased both computational thinking and a deeper comprehension of science and mathematics. Additionally, teachers expressed their enthusiasm to persist new method of instruction. With these future promising results, some STEM teachers have already explored further avenues to integrate computational thinking into their teaching.

Ching & Hsu (2023) explored the development of CT skills through robotics among young learners. Their review demonstrates a substantial increase in research activity, with 82% of the reviewed studies published after 2019. Moreover, 82% of the studies analysed took place in formal educational settings, suggesting that use of CT in school environments and classroom instruction. This research provides insights that can inform the use of CT when solving some problems. The literature reviewed has confirmed the efficacy of CT activities in fostering sequencing, decision, and looping skills among the early lower primary school classes, specifically those in Grade R to 3. This skill is of paramount importance in the early stages of mathematical and literacy development (Kazakoff & Bers, 2012).

The fusion of STEM education and CT equips individuals with the essential skills of analysis, problem-solving, and creative enhancement needed in the twenty-first century. Wing (2006) introduced the concept of computational thinking (CT) and argued that it should be considered a fundamental part of basic education. Thus, CT was described as a problem-solving process that involves breaking down problems into smaller steps, identifying patterns, and algorithms, and evaluating solutions (Huang & Looi 2021). Wing advocates the use of CT as it is increasingly being used in a wide range of fields, including science, technology, engineering, mathematics (STEM) and manufacturing industry. Teachers are key players in this equation, acting as facilitators and mentors. Their challenges encompass not only a deficiency in knowledge regarding specific disciplinary terminology but also a grasp of the context in which these terms are applied, particularly within a computational framework (Paucar-Curasma, Cerna-Ruiz, Acra-Despradel, Villalba-Condori, Massa-Palacios, Olivera-Chura & Esteban-Robladillo, 2023). To effectively use CT in teaching STEM subjects, there is an evident requirement for enhanced professional development initiatives for in-service teachers, focusing on CT, the basics of Artificial Intelligence (AI) and Machine Learning (ML). Furthermore, there is a need to underscore how the STEM design challenges undertaken with students can effectively harness CT for problem-solving. Additionally, STEM teacher educators should explore avenues to integrate CT and create learning experiences that foster the understanding of AI and ML fundamentals within teacher preparation and education coursework (Asunda et al., 2023). This entails offering pre-service teachers opportunities to cultivate competencies and knowledge in CT, encompassing AI and ML, equipping them to effectively guide their future students.

CT plays a pivotal role in problem solving in the contemporary teaching and learning. Currently, it far outweighs the traditional pedagogies characterised by one-size-fits all, where learners are taught but under one nest, despite their individual different needs. It can be used to enhance learners’ techniques to tackle abstract problem-solving skills and promote educational sustainability by applying practical skills in working out solutions to the problem. Learning for a long period of time as it accommodates individual need, and each learner approaches the problem at his/her best understanding abilities. It can create an environment that fosters a lifelong passion for science, technology, engineering, and mathematics (STEM) subjects and make learners feel involved in solving problem. It cultivates innovative thinking that can be used to address real-world challenges, together with skills needed for world that is already driven by digital technologies. The application of four phases of CT, that is, (1) Decomposition, (2) Pattern Recognition, (3) Abstraction, and (4) Algorithm Design, are the key enablers that can propel learners to have multifaceted skill sets needed in the STEM subjects. Teaching learners some basic programming skills using programming languages such as Scratch and Python are likely help them understand and apply CT concepts in other STEM subjects. It is true that programming is node done by many students in South Africa, however, the basic concepts, though seems to be theoretical, they help empowers learners with necessary skills needed to solve problems in other disciplines. Practice makes things perfect, thus acquiring skills during repeatedly doing tasks increase learners’ analytical skills and help increase their innovative thinking capabilities. Once the application of CT is applied in teaching and learning, learners are likely to be immersed into solving some problems. They analyse the problem and come up with some algorithmic steps that may be applied to come up with a solution to the problem. This skill can be nurtured by teaching leaners some basic computational skills that are more practical than theory, ultimately making the CT approach to be an augment teaching approach preparing learners to make an impact and valuable contributions to the to the global community.

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Conflict of interest The authors declare that there is no conflict of interest regarding the publication of this article

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The authors declare no competing interests.

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A Bibliometric Review Leveraging the use of Computational Thinking for educational sustainability in STEM subjects: A Framework for Teaching and Learning in the 21^st Century.

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