¹ Department of Science Education, Universitas Pendidikan Indonesia, Bandung, West Java, Indonesia
² Department of Science Education, Universitas Pendidikan Indonesia, Bandung, West Java, Indonesia
³ Department of Science Education, Universitas Pendidikan Indonesia, Bandung, West Java, Indonesia
⁴ Department of Science Education, Universitas Pendidikan Indonesia, Bandung, West Java, Indonesia
⁵ Department of Science Education, Universitas Pendidikan Indonesia, Bandung, West Java, Indonesia

Yudi Kurniawan
Roles: Conceptualization, Formal Analysis, Investigation, Methodology, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Andi Suhandi
Roles: Supervision, Writing – Review & Editing

Achmad Samsudin
Roles: Supervision, Writing – Review & Editing

Muslim Muslim
Roles: Supervision, Writing – Review & Editing

Riski Muliyani
Roles: Visualization

RQ1. What instructional approaches are used to develop computational thinking in science education?

RQ2. What dimensions of computational thinking are targeted by these instructional approaches?

RQ3. What learning outcomes are reported in the literature?

RQ4. What challenges and future directions are identified for computational thinking integration in science education?

Scopus was selected as the sole database due to its comprehensive coverage of high-quality peer-review publications and its widespread use in bibliometric and systematic literature reviews.

The investigation was conducted utilizing the subsequent query: TITLE-ABS-KEY (“computational thinking”) AND TITLE-ABS-KEY (education OR learning OR teaching OR STEM) AND PUBYEAR >2015 AND PUBYEAR <2026 AND (LIMIT-TO (SRCTYPE, “j”)) AND (LIMIT-TO (PUBSTAGE, “final”)) AND (LIMIT-TO (DOCTYPE, “ar”)) AND (LIMIT-TO (LANGUAGE, “English”)). The literature search was conducted on 31 May 2026.

This systematic literature review is based on a dataset generated from the same Scopus search as used for the bibliometric study on CT in science education. The search resulted 7347 records. Subsequently, several filters were applied, such as the year of publication (2016–2025), document type (journal articles), source type (journals), publication status (published final version of record) and language of publication (English). In total 248 journal articles were included in the curated dataset. From the corpus of 248 articles the studies were filtered again using specific inclusion and exclusion criteria for this review on CT in science education. Finally, 44 studies were included in the qualitative synthesis of this review.

The screening procedure was conducted in accordance with PRISMA 2020 guidelines. The screening was conducted by using Microsoft Excel and Rayyan. After this procedure, articles were screened in three sequential stages: title screening, abstract screening, and full-text eligibility assessment. This task was undertaken to identify studies relevant to the objectives of this review. Studies were included if they empirically investigated the implementation of computational thinking in science education through instructional interventions and reported learning outcomes related to computational thinking.

A set of criteria for including or excluding studies was jointly developed by the research team to ensure that relevant studies were selected and selected studies remained relevant. The criteria for including studies of CT’s evolution were: 1) studies about the evolution of CT; 2) studies about CT that were conducted and reported within a science education framework; 3) studies that included scientific concepts, scientific inquiry, scientific modeling or science-related problem solving; 4) studies that described the instructional methods, pedagogical interventions or learning paradigms for CT; 5) studies that reported results from empirical research; 6) full studies that were published in peer-reviewed journals; and 7) studies written in English.

The studies that are focused on computer science, programming, robotics and engineering without including any science learning were excluded from the study. The studies in which computational thinking is defined and explained but there are no practical applications in terms of instruction were also excluded from the study. The studies that do not contain any CT-related data, review studies, editorials, theoretical studies and studies conducted in the context of science education contexts were not included in the study. The detailed inclusion and exclusion criteria of the studies selected in this study are presented in Table 1.

At the screening stage, all of the selected studies were evaluated using predefined criteria for inclusion and exclusion. Studies that did not deal with the topics of computational thinking and science education and/or lacked information on instructional interventions were excluded from analysis. 51 studies in total were identified as relevant for the screening and were subsequently analyzed at full-text level. During full-text analysis 7 studies were excluded because they were labeled as STEM interventions but were actually restricted to issues of programming, robotics, engineering design and technology without including science learning content. The 44 studies, which in the full-text analysis met all of the predefined criteria for inclusion, were then analyzed and synthesized. A complete study selection overview is given in the PRISMA flow diagram (Fig. 1).

A data extraction form was designed in a structured format based on the four studies. All the data were collected and organized into four main groups of bibliographic data, educational data, pedagogical approaches and dimensions of computational thinking, results, problems for implementation and future research studies related to the studies. The four research studies were used as four established analytical study domains for the data. The results of thematically analogous findings were systemically organized into superordinate categories through inductive theme classification within each of the study domains. Through continuous comparison of comparable concepts between studies, the results were systematically categorized into superordinate categories. A consistent coding framework was developed in the end and presented in Table 2. This framework was then used for organization of extracted data through corresponding categories.

The findings from the included studies were analyzed by means of a coding system using a description and thematic synthesis. For RQ1 the teaching approaches were grouped thematically and described, and the frequency of the single approaches were analyzed. For RQ2 the single approaches were analyzed by means of CT-dimensions abstraction, decomposition, and algorithmic thinking. For RQ3 the single approaches were analyzed by means of the cognitive, emotive and behavioral domains of science learning and the aspect of “integrated learning” by means of CT in order to make sense of science information. For RQ4 the single approaches were analyzed thematically in order to identify problems and future directions of using CT in science teaching from the perspective of the teacher’s computational thinking pedagogical content knowledge (CTPCK). The single approaches for cultivating CT in science education by means of the single instructional approaches, the CT-dimensions as well as the single science learning outcomes were analyzed and integrated. Two researchers coded a subset of articles. The disagreements that occurred were solved by discussion in order to ensure dependability according to the qualitative synthesis protocols.

All research questions (RQ1–RQ4) were coded non-mutually exclusive to allow for a complete literature synthesis. The integrated approach of science-CT education is complex and thus studies may employ a hybrid approach (A13, A44), investigate different cognitive dimensions (A14, A41) and/or report on different outcomes. Hence, an item could be assigned to “Robotics” and “5E Model” for instance, depending on the query syntax applied in a study on tactile computing. “Computational Modeling” for example can comprise “Data Practices” and “Algorithmic Thinking” (A43) and thus the coded CT dimensions (RQ2) are non-mutually exclusive as well. The cumulative frequencies for each category of coded data thus may exceed the number of studies (n = 44). This option allows for a more in-depth account of the different ways in which CT can be developed in integrated science-CT education within various contexts by means of qualitative synthesis.

Table 3 displays all the pedagogical categories that have been developed by inductive topic coding. There are 44 studies which have been analyzed in terms of these categories. The largest number of studies are based on computational modeling (n = 13). This is followed by project-oriented learning (n = 10), educational robotics (n = 9), context-specific pedagogy (n = 6), constructionism/making (n = 6) and 5E learning cycle/inquiry-oriented learning/argumentation learning (n = 5). In summary, the scientific education by means of CT training is very diverse.

The synthesis shows that most of the reviewed studies use computational modeling as the main educational approach to foster CT in science education. As a result, there is a shift from mere programming to modeling for science and modeling of science in order to support the construction of scientific knowledge. Most approaches of CT focus on coding as the main activity, but in recent approaches, CT is supported by using computational models to represent, to model or to explain scientific phenomena (Li et al., 2025; Kubsch & Hamerski, 2022; Omar et al., 2017). The use of simulations, agent-based models, and digital modeling environments in order to have students develop computational procedures and to construct scientific explanations for given phenomena by analyzing dynamic relations between variables within said models, is a current trend in order to have students create models of scientific phenomena. This current trend is supported by the fact that recent developments in natural science, where models are generated, tested and modified in order to generate, test and refine scientific explanations of complex natural phenomena, for constructing evidence, for data anlysis, are being mimicked by computational models (Sun et al., 2026; Dabholkar et al., 2025; Sun L. et al., 2023). Consequently, computation is no longer treated merely as a technical skill but increasingly functions as an epistemic tool for scientific representation, reasoning, and inquiry. So, the integration of computational modeling into classroom practice led to a wider shift in how computational thinking is taught.

The emergence of variety of pedagogical models to implement modeling in science teaching are currently being worked out. This first overview lists FACT, McodeE, ExPreSsion and SCT Tree. It becomes clear that the integration of computational thinking and science education is still a field in full development. The large number of proprietary approaches indicates this (Yun & Crippen, 2025; Vieyra et al., 2024). While there are many lesson design frameworks that address particular scientific content for particular student ages working with particular software, there is no current integrated approach to teaching. So, the problem of instructional integration remains. However, there are an increasing number of examples of computational modeling lessons as well as of lessons that use an inquiry-based approach in order to teach CT to students and in doing so, allow them to practice the scientific practices that are at the core of CT (Richter et al., 2022; Psycharis, 2019; Grizioti, 2025).

Table 4 summarizes the CT dimensions addressed across the reviewed studies. The most dimension of CT are Decomposition, Abstraction, Pattern Recognition, Algorithmic Thinking. The Computational modeling and simulation also appeared most frequently. The noticeable finding is the emergence of system thinking in CT dimension in 5 articles.

It is important to note that computational thinking in science education is expanding conceptually. Classic framework of computational thinking was concentrated on decomposition, abstraction, pattern recognition, and algorithmic thinking. In contrast with this synthesis result that dimension of computational thinking is expand to computational modeling, systems thinking, data practices, creativity, and interdisciplinary problem solving (Hurt et al., 2023; Kafai & Proctor, 2022; Weintrop et al., 2016). This evolution implies the growing integration of computational thinking with scientific practices rather than programming activities alone.

The implications of it are students should be enabled to develop their modeling and simulation skills as well as their data skills for scientific inquiries instead of learning various programming languages (Sun et al., 2026; Grizioti, 2025). Second, assessments of CT must also be measured in all its aspects and not only algorithmic thinking (Yuana et al., 2025; Lu et al., 2022; G. Chen et al., 2017). CT should be integrated into the science instructional design for scientific inquiry so that CT is used by students as tools to explain and investigate scientific and real-world problems. CT should be integrated into the science instructional design for scientific inquiry so that CT is used by students as tools to explain and investigate scientific and real world problems (Yuana et al., 2025; Ogegbo & Ramnarain, 2022; C. Wang et al., 2022a).

Also, the results of the preceding question are confirmed that computational thinking expands by the usual teaching arrangement. The dimensions of the computational thinking do not show up in all teaching situations. So, with respect to the learning tasks of the teaching techniques different instructional methods target different dimensions of the computational thinking. This regularity is described by the influence of the teaching design for the emerging computational thinking skills (Adorni et al., 2025; Palop et al., 2025; Kong, 2016). Table 5 shows the prevalent alignment between instructional methodologies and computational thinking dimensions across reviewed research.

This review does not only present various approaches for teaching computational thinking and corresponding dimensions of CT, but it also explains the relationships between them in more detail. The specific teaching approaches support specific dimensions of CT (Fayanto et al., 2024; Deng et al., 2020). All of these approaches to learning are natural to their respective methodologies. The computational modeling of scientific systems supports systems thinking, model building, and evidence-based reasoning over algorithm implementation. Inquiry-oriented learning approaches, such as scientific investigation, typically have students abstract important factors of interest from systems under investigation; analyze data; and explain observations of systems. In contrast, approaches such as robotics-based education support algorithmic thinking and debugging by implementing a series of steps in a program, testing the program’s logic, finding bugs, and “tweaking” a solution to a problem. Project-based approaches support a variety of approaches to addressing real scientific problems through collaborative computational design and learning in real scientific work.

This study show that in addition to supporting different aspects of CT equally, different pedagogical approaches support different dimensions of CT based on the epistemic processes and learning activities they support. Therefore, rather than choosing an approach for reasons of available technology, current trends in curricula etc., one should choose an approach on the basis of the CT competencies one wants to support and for which there is strong alignment of goals, learning activities, assessment and outcomes of CT.

This information imply an implication for teachers. All teachers have to consider the specific CT dimension they intend to develop rather than selecting instructional approaches based on curriculum trends or technological availability. The information of alignment can support learning process from preparation until evaluation in the end of class (Fig. 2).

The findings indicate that CT is a learning outcome that can be supported by pedagogical approaches, but it is not merely a set of cognitive skills to be learned. The various aspects of CT are manifested in different ways depending on the specific epistemic demands of different learning environments (Zhang et al., 2024; Melro et al., 2023; Deng et al., 2020). Thus, while there are many studies that report on the development of CT, the results are inconsistent. Future design of CT instructional approaches should not focus on whether or not CT is being developed, but on which aspects of CT are most relevant to support students’ abilities to achieve specific scientific learning goals.

This review reveals that introducing CT to science education can lead to a variety of learning outcomes, far beyond simple calculations. The results of the reviewed studies presented in Table 6 reveal a range of very positive outcomes for students, including their CT skills, their understanding of science, their affective development, their higher-order thinking skills, and their scientific sense-making, to name but a few. All of these outcomes can be fostered in a variety of ways and thus, CT is turning into a very valuable pedagogical tool to support science education through a variety of cognitive and affective results.

RQ3 was concerned with the outcomes reported by the students. As mentioned before for RQ1 and RQ2, the results were balanced with respect to the distribution of reported outcomes. In particular, there were reported positive effects on the students’ skills in computational thinking as well as on their science knowledge. Finally, there were also positive effects on the affective outcomes (i.e. students’ motivation for learning, engagement, and confidence to learn) due to computer science teaching. Computer science teaching is no longer intended to make students computing competent; instead, it is integrated in the teaching of the sciences in order to support the construction of scientific knowledge by means of computational practices.

Several different approaches to epistemic teaching can explain the variety in learning results (Barzilai & Chinn, 2024; Gheyssens et al., 2022; Schuster et al., 2018). Students use computational modeling and simulation to graphically represent abstract scientific processes, test hypotheses by varying parameters, and to study the dynamics of complex systems in detail. By means of inquiry-based learning of scientific reasoning and CT, students learn to ask questions, to interpret data, and to explain phenomena. In project-based and collaborative learning environments, students communicate with each other, work together, are creative and solve problems using computer tools. Thus, in a concrete setting of real scientific problems, different teaching methods can foster different aspects of student learning and thus yield a wide range of educational benefits (Pinar et al., 2025; Rani, Dolly, 2025; Schuster et al., 2018).

There is another finding that the growing prominence of integrated scientific sense-making as a learning outcome. Integrated scientific sense-making was reported less frequently than the other categories of outcomes for CT in science education. However, it is one of the key outcomes for science education that CT can produce. Rather than solving a computational problem, students use computational representations to explain scientific phenomena, to compare and evaluate different explanations of phenomena, to make sense of data by interpreting it, and to construct scientific arguments from evidence (Wiese et al., 2024; Voon et al., 2022; Pallant & Lee, 2015). Thus, this outcome supports our prior assertion that in science education, CT functions as an epistemic practice for generating and communicating scientific knowledge rather than as another technical practice such as learning to program (Hurt et al., 2023; Krakowski et al., 2023; Weintrop et al., 2016).

The educational value of CT for students’ learning of science goes beyond them becoming computationally competent. This analyzed show that the experience or practice in class can support students’ conceptual understanding, scientific reasoning, motivation, collaboration and higher order thinking using a variety of scientific acitivities. The outlined in the above synthesis note is promotion of scientific literacy and/or computational literacy are two ways in which CT can be of value to science education. CT is not a learning outcome in itself. It is a method/approach to science education which can promote a more meaningful and open science education through an inquiry approach (Farris & McLaughlin, 2024; Braun & Huwer, 2022).

This review outlines current research on problems that impede the successful implementation of CT in classrooms. We already mentioned the positive results of RQ3 in which a range of studies provide evidence of the positive effect of CT on learning. The problems outlined in RQ1, RQ2 and RQ4 all relate to the implementation of CT in classrooms. Table 7 summarizes challenges and future directions to implement CT in science education.

RQ1 indicates that the main problem of the implementation of CT in the classroom is the teacher preparation for teaching using computational practices and for connecting these practices with scientific inquiry in learning. In many cases, teachers are well prepared to teach about science, or they are well prepared to teach computation, but they lack preparation to support their students to integrate computational practices with scientific inquiry in learning for computational thinking. In addition, problems that students’ cognition present for CT are transdisciplinary in nature. In learning, students are expected to apply a range of scientific concepts, different computational representations, data interpretation and various aspects of algorithmic thinking. The demands of these different aspects on students’ working memory can pose a problem, so scaffolding in instruction is necessary to deal with these problems (D. Chen et al., 2026; Yang et al., 2025; Tawfik A.A. et al., 2024; Moore et al., 2020).

While there is positive learning outcomes reported for many studies (RQ3), also a number of obstacles for an integration of computational thinking into the curricula of formal science classes have been made known (RQ4). The positive results from controlled studies are hardly replicable on a larger scale. Students experience problems in managing their cognitive load while learning programming logic and corresponding science content at the same time. Teachers experience problems with their instructional readiness for integrating CT into their regular instruction.

A persistent difficulty with evaluation pertains to computational thinking pedagogical content knowledge (CTPCK). Assessing students’ computational thinking processes (CT) while they are learning to apply CT in science teaching (CT PCK) assessment requires a different type of assessment. Students will demonstrate processes of, for example, abstraction, systems thinking, modeling and iterative testing and revising while problem solving in CT-based science lessons. Thus, the many aspects of CT that students can learn will not be measured by typical assessments.

The isolation of single classroom interventions is no longer sufficient when it comes to future education research on computational thinking. Rather, sustainable ecosystems for education are needed. It is not sufficient that single instructional approaches are effective (Liu et al., 2024; Lee et al., 2022; Kite et al., 2021). The future research is needed that looks at issues of alignment with curricula, teacher professional development, inclusive participation as well as at issues of sustainability and long-term implementation. In the future, research on computational thinking in education should go beyond measuring the effectiveness of education and look at issues of educational scalability. Moreover, from a disciplinary perspective, most studies on the implementation of computational thinking in science education are conducted in the domain of physics or in integrated STEM contexts. The biological and chemical sciences are strongly underrepresented in the reviewed studies. Future studies thus have to investigate how computational thinking can be implemented in a truly disciplinary way in these underrepresented sciences by means of discipline-specific instructional approaches for computational thinking.

The development of CT research in science education is dependent on two parallel developments: On the one hand, the design of appropriate instruction and on the other hand the setting up of an adequate and coherent educational system. This also includes curriculum, teachers’ long-term professional development, diagnosis and learning evaluation as well as equal access to science education. If these system-related requirements are not met, learning gains cannot be sustained and cannot be implemented in other classrooms (Madariaga et al., 2023; C. Wang et al., 2022b). Finally, all findings of this study suggest that the future of CT in science education is not just going to be addressed by introducing new forms of instruction but rather by establishing complete educational systems (ecosystems) that support pedagogy, curriculum, teacher professionalization, assessment and equal access to high quality resources. While there is an intense development of a wide variety of new forms of instruction for computational thinking, the setup of curricula, teacher education and assessment is not developing at a similar pace. This difference needs to be in the focus of future research on computational thinking in science education.

This review has a number of limitations. The search was restricted to the Scopus database. As a result, studies not listed on Scopus have been excluded from this review. No assessment of risk of bias has been conducted as this review seeks to provide a qualitative thematic synthesis of the findings from included studies. Readers must be aware of these methodological decisions when interpreting the findings from this review.

Authors would like to acknowledge the Indonesian Education Scholarship, Center for Higher Education Funding and Assessment, and Indonesian Endowment Fund for Education for their support of this study.

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