Computational thinking (CT) is a problem-solving approach used by computer scientists — but applicable to all fields. It has four pillars: decomposition, pattern recognition, abstraction, and algorithm design.
Decomposition: break a complex problem into smaller, manageable parts. Example: making a game → design characters, design levels, write controls, add scoring. Pattern Recognition: spot similarities and trends. Example: multiplication table — each row follows the same pattern. Abstraction: focus on what matters, ignore irrelevant details. Example: a map shows roads, not every tree. Algorithm: step-by-step procedure to solve a problem. Must be clear, finite, and produce correct output.
Algorithm: written in plain language (pseudocode). Example: find largest of 3 numbers — Step 1: Read A, B, C. Step 2: If A>B and A>C, largest=A. Step 3: Else if B>C, largest=B. Step 4: Else largest=C. Flowchart symbols: oval (start/end), rectangle (process), diamond (decision), parallelogram (input/output), arrows (flow). Draw flowcharts for: login system, calculator, grade assignment.
CT skills are universal problem-solving tools. Decomposition helps in any complex project (event planning, research papers). Pattern recognition helps in science (periodic trends), mathematics, and daily life. Abstraction helps focus on key information (writing summaries, creating models). Algorithm design helps create efficient procedures (recipes, assembly instructions). Top companies like Google specifically test for CT skills in interviews.
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