BEYOND CODING: HOW TO ACTUALLY TEACH COMPUTATIONAL THINKING IN SCHOOLS

Computational Thinking Is Not Coding

CT is commonly misunderstood as being equivalent to the practice of programming. However, the reality is far from this as CT is more of a pre-coding problem-solving paradigm, as proposed by Wing (2006).

Wing argues that CT is a fundamental form of literacy, just as reading, writing, and arithmetic, essential for navigating modern scientific and societal complexity (Wing, 2006).

What is important to educators is not the value of CT, but the question of how to teach CT in a systematic way.

The Plugged versus Unplugged Computational Thinking Paradigm

Another commonly held fallacy is the idea that CT inherently involves computer-mediated activities. Empirical evidence suggests the opposite: Unplugged CT provides a foundation before computer-mediated activities occur (Galanti & Holincheck, 2024).

Unplugged CT (Cognitive Foundations)

• Writing algorithms for making a sandwich
• Sorting physical objects (pattern recognition)
• “Human robot” precision instruction activities

Plugged CT (Automation & Simulation)

• Scratch programming
• Robotics programming (LEGO Spike, Micro: bit)
• Data modeling in spreadsheets or simulations

Unplugged CT reduces cognitive load and promotes abstraction before the introduction of formal coding instruction.

Integrating Computational Thinking (CT) within the Engineering Design Process (EDP)

CT is not to be taught as an independent computer science course. Its power is realized when the entire cycle of the following engineering problem-solving methodologies is considered:

Identify → Design → Create → Test → Improve

The Engineering Design Process naturally incorporates algorithm design, debugging, and other aspects of computational thinking (National Research Council, 2009; Galanti & Holincheck, 2024).

Model-Eliciting Activities (MEAs): Translating Reasoning

Model Eliciting Activities (MEAs) challenge the student to create models for real-world clients, thereby making the student’s reasoning processes visible and transportable across disciplines.

Example: Countdown Timer Project

• Unplugged: Students design logic models
• Plugged: Implement in block-based code
• Debugging: Identify logic mismatches

MEAs promote persistence, tolerance for ambiguity, and skills in system modeling, which are all aspects of a way of thinking called computational thinking.

Block-Based Programming as a Cognitive Scaffold

Environments like Scratch or Blockly are block-based programming environments that help eliminate the complexity of programming language syntax, allowing the student to focus on logic rather than programming language details.

The blocks in Scratch correspond to the following fundamental computer science (CS) concepts:

• Events → Causal reasoning
• Loops/Conditionals → Control flow
• Variables → Abstraction and data representation

Use → Modify → Create Framework

• Use existing systems
• Modify parameters and logic
• Create original computational artifacts

Such a progression enables the learner to make a shift from a passive technology consumer to an active computational creator.

The Four Pillars of Computational Thinking

It is recommended that teachers should explicitly teach students about the cognitive architecture of computational thinking:

- Decomposition: This is the breaking down of complex problems into their various, more manageable parts.

- Pattern Recognition: The recognition of patterns to guide solution strategies.

- Abstraction: The process of eliminating unnecessary details to emphasize important features.

- Algorithmic Thinking: A process for defining sequential processes that can be executed by both humans and machines.

Collectively, these pillars comprise the mental operating system that underlies contemporary STEM cognition.

Debugging as Cognitive and Affective Competence

This is because debugging redefines failure in terms of information content. A bug identifies a discrepancy between the desired action and actual system response, which is a basic assumption in scientific epistemology, according to Wing (2011).

Intentional debugging strategies in which pre-embedded errors are addressed promote student resilience and epistemic confidence.

The OSSM: A Conceptual Model of Education in Computational Thinking

I propose the OSSM as a way to conceptualize CT education as a stratified system with four interconnected layers:

Layer 1: Cognitive Systems

• Decomposition,
• Abstraction
• Pattern recognition
• Algorithms

Layer 2: Pedagogical Systems

• EDP cycles
• MEAs
• Use-Modify-Create

Layer 3: Technological Systems

• Block-based coding
• Robotics
• Data modeling tools

Layer 4: Societal Systems

• Real-world problem contexts
• AI literacy
• Policy and ethics integration

In the OSSM model, the traditional view of CT as a single subject is replaced by the view of CT as a multi-layered cognitive-pedagogical infrastructure.

The Bottom Line

Computational thinking is the cognitive operating system of the AI era.

References

Galanti, T. M., & Holincheck, N. M. (2024). Integrating computational thinking in elementary STEM using the engineering design process. School Science and Mathematics, 124(4), 279–286.

National Research Council. (2009). Engineering in K–12 education: Understanding the status and improving the prospects. National Academies Press.

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. Wing06-ct

Wing, J. M. (2011). Computational thinking—What and why. The Link Magazine.