Computational Thinking

 

Computational Thinking in 13 Points

1. Promotes structured problem-solving — encourages logical approaches to complex issues.

2. Decomposition — breaking a large, complex problem into smaller, manageable parts.

3. Pattern recognition — identifying similarities or trends to simplify problem-solving.

4. Abstraction — focusing on essential information while ignoring unnecessary details.

5. Algorithm design — developing step-by-step procedures to reach a solution efficiently.

6. Enhances logical reasoning — trains learners to think sequentially and analytically.

7. Encourages efficiency — seeks optimal paths to solutions using minimal resources.

8. Supports debugging mindset — teaches how to detect, analyze, and fix errors systematically.

9. Applies across disciplines — not limited to computer science but useful in all learning areas.

10. Builds resilience — promotes persistence through trial, testing, and improvement.

11. Facilitates automation thinking — helps design solutions that can be executed by machines.

12. Develops transferable skills — nurtures strategic, creative, and adaptive thinking for real-world problems.

13. The key principle — the solution to the large problem emerges from the solutions to smaller ones, reflecting how computers and humans tackle complexity step by step.