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1. Computational Thinking

Computational Thinking is a disciplined approach to problem-solving that emphasizes clarity, efficiency, and systematic reasoning.

This framework is built on four core skills that provide a versatile framework to strengthen problem-solving in programming, mathematics, engineering, and everyday life.

1. The Four Pillars

1.1. Decomposition

Breaking a complex problem into smaller, more manageable parts.

flowchart TD
    A[Complex Problem] --> B[Part 1]
    A --> C[Part 2]
    A --> D[Part 3]
    A --> E[Part 4]

1.2. Pattern Recognition

Finding similarities, repetitions, or trends among the smaller parts.

flowchart LR
    A((Part 1)) --> X[Pattern]
    B((Part 2)) --> X
    C((Part 3)) --> X
    X --> D((General Rule))

1.3. Abstraction

Removing unnecessary details to focus only on what matters.

flowchart LR
    A[All Details] -->|Remove Clutter| B[Essential Ideas]

1.4. Algorithmic Design

Developing a precise, step-by-step plan to solve the entire problem or its sub-parts.

flowchart TD
    A[Start] --> B(Step 1)
    B --> C(Step 2)
    C --> D(Step 3)
    D --> E[End]
2. Commented Examples

2.1. Decomposition and Pattern Recognition

Problem: Find the sum of all numbers from 1 to 200.

Decomposition: Pair numbers from opposite ends:
1 + 200
2 + 199
3 + 198
...

Pattern Recognition: Each pair sums to 201.

Algorithmic Design:

  1. Create number pairs.
  2. Count the number of pairs (200 ÷ 2 = 100).
  3. Multiply: 201 × 100 = 20,100

2.2. Abstraction

A bus route planner ignores irrelevant details such as:

  • building colors
  • parked cars
  • tiny alleyways

Planners keep only:

  • main streets
  • stops
  • distances
  • times
3. Short Practice Exercises

3.1. Decomposition

List four smaller tasks needed to organize a school Science Fair.

Show solution
  • Reserve the venue.
  • Assign booths.
  • Prepare schedule and logistics.
  • Create judges' materials.

3.2. Pattern Recognition

Identify the pattern in: 10, 20, 30, 40, 50...

Show solution

This is an arithmetic sequence increasing by 10.

3.3. Abstraction

A programmer builds a BMI calculator. Which details matter? Which do not?

Show solution
  • Essential: weight, height.
  • Not essential: hair color, music preference, phone number.

3.4. Algorithmic Design

Write an algorithm for checking out a book from a library.

Show solution
  1. Start.
  2. Pick a book.
  3. Go to librarian.
  4. Scan book.
  5. Scan library card.
  6. Confirm checkout.
  7. End.
4. Mini-Quiz

4.1. Which of the following best defines computational thinking?

A) A method for memorizing programming syntax.
B) A technique for translating code into natural language.
C) A set of methods for expressing problems and their solutions.
D) A way to optimize hardware performance.

Show answer

C) A set of methods for expressing problems and their solutions.

4.2. Breaking a complex challenge into smaller parts refers to:

A) Abstraction
B) Decomposition
C) Pattern Recognition
D) Algorithmic Design

Show answer

B) Decomposition

4.3. Showing only the Sun and planets to explain orbits is an example of:

A) Decomposition
B) Debugging
C) Algorithmic Design
D) Abstraction

Show answer

D) Abstraction

4.4. An algorithm is:

A) A list of random examples.
B) A precise, step-by-step procedure.
C) A collection of ideas.
D) Code written in Python.

Show answer

B) A precise, step-by-step procedure.

4.5. Finding similarities among sub-problems refers to:

A) Decomposition
B) Abstraction
C) Pattern Recognition
D) Debugging

Show answer

C) Pattern Recognition

5. Common Mistakes
  • Confusing algorithm with program.
  • Using vague steps ("do it", "fix it").
  • Mixing Decomposition (break apart) with Abstraction (filter out details).
  • Keeping irrelevant details.
6. Summary Mindmap 
mindmap
  root((Computational Thinking))
    Decomposition
      Break into parts
      Manage complexity
    Pattern Recognition
      Identify similarities
      Predict behavior
    Abstraction
      Remove noise
      Focus on essentials
    Algorithmic Design
      Step-by-step plan
      Precise instructions
7. Optional Extensions
  • Decompose a daily-life problem.
  • Identify patterns in a sequence you choose (e.g., even numbers, repeating schedules).
  • Write an algorithm for a simple routine.