The problem the researchers set out to address is well documented. Globally, digital technologies have become a core element of primary teacher training, with organizations such as ISTE and UNESCO pushing for classroom technology competence, and with curricula like Australia’s demanding that students develop algorithmic thinking and problem-solving skills embedded in authentic contexts. Yet studies of initial teacher education consistently reveal that programs emphasize technological knowledge while neglecting technological pedagogical knowledge, the crucial understanding of how to teach with and about technology. Without that, pre-service teachers tend to fall back on generic strategies, reproducing commercial step-by-step coding activities without conceptual understanding, or relying on loosely connected, activity-based lessons that look engaging but lack instructional coherence.

The research team, led by Peter Curtis, Michael D. Carey and Natalie McMaster of the University of the Sunshine Coast, together with David A. Martin of Edith Cowan University, points to striking evidence of this gap. In a frequently cited study by Bower and Falkner, when pre-service teachers were asked to list pedagogical strategies for developing students’ computational thinking, most simply equated computational thinking with using technology, and only one explicitly mentioned coding. Even more telling, the candidates reported high confidence in their abilities, a phenomenon the researchers describe as a third order of ignorance: being unaware of one’s own lack of pedagogical knowledge. Systematic reviews of computational thinking in teacher education reach similar conclusions, finding that translating confidence with digital tools into meaningful classroom practice remains a persistent challenge.

The 3C Model, whose three Cs stand for Context, Capabilities and Computational focus, emerged from an earlier qualitative study by Martin, Curtis and Redmond that examined how primary school students learned coding and computational thinking through the framework. That study used a triangulated design combining semi-structured focus-group interviews, analyzed through Clarke and Braun’s thematic analysis with NVivo software, and an insider researcher’s systematic observations and reflective field notes analyzed through Schön’s framework of reflection-in-action and reflection-on-action. The findings showed that the model enhanced student engagement and supported curriculum learning outcomes, and subsequent work by Wang and Kale highlighted its potential as a developmentally appropriate approach for building foundational computational thinking skills in pre-service teachers.

What distinguishes the 3C Model is its explicit grounding in established learning theory. The framework is anchored in Piaget’s theory of cognitive development, specifically targeting children aged around 10 and 11 who sit near the transition from concrete operational to formal operational thinking. At this stage, learners can reason logically about tangible experiences but still rely heavily on concrete, contextualized material to support cognitive processing. The model also builds on the Concrete-Representational-Abstract instructional sequence and the Language Model, both drawn from mathematics pedagogy and rooted in Bruner’s theory of enactive, iconic and symbolic representation. The researchers are candid that TPACK, the influential framework describing the intersection of technological, pedagogical and content knowledge, tells teachers what they need to know but does not prescribe how to sequence instruction. The 3C Model fills that translational gap.

In practice, the model unfolds across a five-lesson sequence. Lesson 1, Context, introduces curriculum content through an authentic, culturally appropriate problem scenario that integrates digital technologies with other learning areas such as science, health or mathematics. Lesson 2, Capabilities, has students explore what a digital tool such as Scratch or a micro:bit can actually do, without writing any code, while the teacher elicits pre-coding language like if-then and repeat until through structured discussion. Lessons 3 and 4, the Computational focus, support a deliberate and gradual shift from student-familiar everyday language to the formal abstraction of coding and algorithmic thinking, with students acting out algorithms physically before translating their pseudocode into block-based programs. Lesson 5 invites students to create a unique artifact through project-based learning, applying their new skills to a problem of personal or community relevance.

Two design choices make the framework particularly notable for equity. First, because individual devices are not central to developing computational thinking in the early lessons, students can engage meaningfully with complex coding concepts in a largely unplugged environment, allowing schools with limited computing resources to participate fully. Second, the delayed introduction of abstract code gives teachers natural opportunities to differentiate instruction, supporting learners who need concrete representations, language support or extra scaffolding. The authors provide worked examples spanning year levels, including an upper primary unit integrating health, English, mathematics and digital technologies using Scratch, and a lower primary beach safety sequence that weaves together the Blue-Bot block-coding app, mathematics and English, alongside a marking rubric and assignment templates hosted in an open science repository for other teacher educators to adopt.

The team also reports preliminary evidence from an exploratory implementation, in which the 3C Model was embedded into coursework as pedagogical learning and assessment tasks at two Australian universities. While the authors are careful to characterize this as an informal investigation rather than a formal research study, pre-service teacher feedback suggested increased confidence and improved instructional skills in teaching coding and computational thinking. The researchers stress, however, that prerequisites matter. Programs should introduce the model only after candidates have studied TPACK and the Concrete-Representational-Abstract approach or Language Model in their technologies and mathematics coursework, since understanding the gradual release of responsibility and the principles underlying those sequences is critical to teaching coding through the model’s structure.

The implications extend beyond individual classrooms. The authors argue that embedding digital competence as a core professional expectation should be supported by incorporating frameworks like the 3C Model into accreditation and curriculum standards, and they connect the work to the United Nations Sustainable Development Goal 4 on quality education by framing it as a route to equitable access to high-quality teacher preparation. Future research, they say, should investigate the model at scale, including longitudinal exploration of pre-service teacher confidence, pedagogical shifts and student outcomes. If those studies bear out the early promise, a model that asks children to walk an algorithm before they type one may become a standard fixture of how the next generation of teachers learns to teach the language of computers.

Subject of Research: A pedagogical framework for teaching coding and computational thinking in teacher education

Article Title: Advancing digital technology teacher education through the 3C model

Article References: Advancing digital technology teacher education through the 3C model. (n.d.). https://doi.org/10.1007/s44322-026-00056-1

Image Credits: AI Generated

DOI: 10.1007/s44322-026-00056-1

Keywords: teacher education, computational thinking, coding, 3C Model, TPACK, pedagogy, pre-service teachers, digital technologies, Piaget, unplugged learning, curriculum integration, primary school