Computational Thinking

Computational Thinking

> Technique used to understand problems and develop possible solutions before using a computer.

> Helps break down complex problems, understand them, and present solutions in a way computers, humans, or both can understand.

Complex problem

A problem that is not easily solved at first glance.

The Four Cornerstones of Computational Thinking

decomposition, pattern recognition, abstraction, algorithms

Decomposition

breaking down a complex problem or system into smaller, more manageable parts

Pattern Recognition

looking for similarities among and within problems

Abstraction

focusing on the important information only, ignoring irrelevant detail

Algorithms

developing a step-by-step solution to the problem, or the rules to follow to solve the problem

Computational thinking enables you to work out exactly what to tell the computer to do.

true

Defining the problem in problem-solving involves turning an undesirable start point into a desirable goal.

True

Examining the start point helps clarify what the goal should be.

True

If a process is too slow, the goal may involve automating decisions.

False - the goal would involve improving the process speed.

Missing information about how something behaves suggests creating a model or simulation.

True

Work Backwards

A problem-solving strategy where you start at the goal state and work backwards, stage by stage, deducing what is required to reach each preceding stage.

Pattern Recognition

A key step in computational thinking that involves identifying repeated or regular ways something happens.

pattern

repeated or regular way in which something happens or is done.

Types of Pattern Recognition

Across problems & Within a problem

Across problems

Finding similarities among different problems that follow the same pattern.

Within a problem

Finding patterns inside a single problem to reduce repetition in code.

Work Generalisation and Subroutines

After solving a decomposed task, the solution can be generalized for use in other problems or projects.

Subroutines

A reusable, generalized solution that makes problem-solving more flexible.

Abstraction

way of expressing an idea in a specific context whilesuppressing irrelevant details.

Maps are physical abstractions of physical things

True

Information Abstraction

> the process of hiding unnecessary or complex details to focus on essential information

> Each Instance Corresponds toOne Real-World Entity.

> include:Properties: name, value.Types: integer, float, string,etc.

Layering Abstractions

A technique where a problem is represented at different levels of detail.

Key points:

Add a layer: Obscures or hides details.

Peel a layer: Reveals more detailed information.

Can add multiple layers to suppress unnecessary details.

Example: Layers of abstraction in email systems.

Leaking/Lacking Details

When an abstraction hides too many details, it can mislead or fail in practice.

Leaky abstractions must be updated to include important missing details.

True

Modeling

An important type of abstraction in computer science

model

> representation of real-world things that exclude certain details.

> shows the entities that make up your solution, and the relationships between them

Static Models

Show entities and relationships at a specific point in time.

Example: Underground map.

Dynamic Models

> Represent how a problem changes over time.

> Include states, transitions, events, and actions.

Example: State machine model of a turnstile.

Algorithms

> a process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer

> A step-by-step set of instructions with input/output, clear start/end, correct, efficient, and properly ordered.

Instructions / actions / processes

The basic steps in an algorithmVaries in nature depending on the context and level of abstraction

Main Control Structures of Algorithms

Sequencing logic (order instructions), Selection logic (conditional flow), and Iteration logic (repeating instructions based on conditions).

Sequencing logic

executing instructions in order of appearance

Selection logic

The flow of the algorithm may change based on certain conditions (Boolean logic).

Iteration logic

algorithms can use repetitionlogic to execute certain instructions more than once, also based on certain conditions.