Computational Thinking is a problem-solving process that enables students to think, learn and create to solve problems using a systematic approach to the problem or task.
"This is not about wanting everyone to become a computer scientist. Just like the ability to read, it's about computational fluency for everyone and the ability to think and create."
– Dr. Karen Brennan, Harvard School of Education
THE FOUR PILLARS OF COMPUTATIONAL THINKING:
Decomposition
Definition: Breaking down a complex problem or system into smaller, more manageable parts.
Example: When planning a long trip, decomposing the task into "booking flights," "finding accommodation," and "creating an itinerary".
Example: To bake a cake, you break the process into "preparing the pan," "mixing the ingredients," and "baking the cake".
Pattern Recognition
Definition: Identifying similarities, trends, or regularities within or among problems.
Example: A GPS system recognizes traffic patterns to suggest the fastest route.
Example: When sorting a large group of items, you look for patterns in their features to help you sort them more efficiently.
Abstraction
Definition: Focusing on the most important information and ignoring irrelevant details.
Example: When giving directions to a new place, you focus on the main roads and landmarks, not every single house you pass
Example: A book summary on the back cover or a review provides the main plot points and themes, abstracting away thousands of individual sentences and words.
Example: When teaching a computer to play chess, you represent the board and pieces in a way that captures their essential properties while ignoring things like the material they are made of.
Algorithms
Definition: Developing a step-by-step set of instructions or rules to solve a problem.
Example: The steps you follow to get ready in the morning, from brushing your teeth to getting dressed.
Example: Creating a recipe to bake a cake, with precise instructions for each ingredient and step.