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Introduce the course structure, learning goals, assessments, and expectations. Explain that computational thinking is a way of solving problems and is not the same as programming.
Define computational thinking as a structured approach to problem solving. Provide everyday examples such as planning a trip, organizing a room, or following a recipe.
Explore how computational thinking is used in science, business, education, and daily life. Focus on practical benefits rather than technical details.
Teach students how to identify a problem, define goals, and understand what needs to be solved before jumping to solutions.
Explain that every problem has inputs, desired outputs, and limitations. Use examples such as planning a budget or organizing an event.
Introduce trial and error, intuition, experience, and observation. Discuss the strengths and weaknesses of human problem solving.
Explain that computers follow precise instructions and cannot make assumptions. Compare computer reasoning with human reasoning.
Explain how large problems can be broken into smaller parts. Use examples such as planning a vacation or organizing a school event.
Teach students to identify similarities and repeated structures between problems. Use examples from games, routines, and daily activities.
Explain how to focus on important details while ignoring unnecessary information. Use examples such as maps and simplified diagrams.
Introduce the idea of creating step-by-step instructions to solve problems. Use everyday examples without writing code.
Explore algorithms found in daily life such as making tea, tying shoelaces, or preparing for school.
Introduce true and false statements, reasoning, and logical thinking through simple puzzles and examples.
Explain conditional thinking using IF-THEN scenarios. Use examples such as deciding whether to carry an umbrella.
Introduce repeated actions and routines. Explain why repeating instructions manually is inefficient.
Explore different ways information can be represented including text, numbers, symbols, tables, and diagrams.
Teach students how to visualize processes using simple flowchart symbols and decision points.
Introduce pseudocode as a way of writing algorithms in plain language without using programming syntax.
Explain what data is and why information is collected. Use examples such as attendance records and survey responses.
Teach basic methods for gathering, sorting, and organizing information for easier analysis.
Show how useful insights can be discovered by identifying trends, categories, and repeated behaviors.
Introduce simplified representations of real-world systems such as maps, weather forecasts, and flight simulators.
Demonstrate that a single problem can have many solutions and discuss how different approaches may vary.
Explain why some solutions are better than others based on time, effort, and resources required.
Introduce the idea of improving an existing solution to make it faster, simpler, or more effective.
Explain how mistakes occur in processes and introduce systematic ways to identify and fix them.
Teach students to verify whether a solution works correctly under different situations.
Introduce the idea of refining solutions through repeated testing and gradual enhancement.
Explain how multiple components interact to form larger systems such as schools, transportation networks, or ecosystems.
Introduce situations where outputs influence future inputs. Use examples such as thermostats and social media recommendations.
Explore practical applications of computational thinking in science, business, healthcare, education, and everyday decision making.
Choose a simple real-world problem and identify its goals, inputs, outputs, and constraints.
Apply decomposition, pattern recognition, abstraction, and algorithmic thinking to develop a solution.
Evaluate the proposed solution, identify improvements, and reflect on the computational thinking process.