Module 1: Computational Thinking & Logical Problem-Solving 

Objective: Equip students with problem-solving skills using computational thinking frameworks and prepare them for coding and digital innovation.

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Introduction to Computational Thinking

What is Computational Thinking?

Key Topics:

• Definition and importance in digital problem-solving
• The 4 Pillars: Decomposition, Pattern Recognition, Abstraction, Algorithms

 

Decomposition (Breaking Down Complex Problems)

Key Topics:

• Breaking a big problem into smaller, manageable parts
• Identifying inputs, processes, and outputs in a problem

Logical Thinking & Pattern Recognition

Pattern Recognition in Problem-Solving

key Topics:

• Identifying repeated trends in data and processes
• How recognizing patterns simplifies problem-solving

 

Logical Thinking & Flowcharts

key Topics:

• Using logic to create decision-making processes
• Flowchart symbols and their applications in algorithms

Algorithmic Thinking & Problem-Solving

Introduction to Algorithms

Key Topics:

• What is an algorithm?
• Step-by-step logical solutions (with examples like Google Search, GPS navigation)

Writing & Optimizing Algorithms

Key Topics:

• Efficiency in algorithms (time and space complexity basics)
• Optimization strategies (e.g., reducing unnecessary steps)

Applying Computational Thinking to Coding

Translating Logic into Code

Key Topics:

• Bridging computational thinking with coding
• Variables, loops, and conditionals in Python

Computational Thinking in Action

Key Topics:

• Applying all learned skills to a real-world digital problem

This structured plan ensures students develop computational thinking and logical problem-solving skills essential for coding, AI, and digital innovation. Would you like any adjustments or additions?

Source CHatGPT with adaptations to suite the program