Computational Thinking

Computational Thinking

Computational Thinking - Lesson 1

S2 Computing Science - Remote Learning

Learning objectives:

• understand what computational thinking is

• know what the four cornerstones of computational thinking are

• successfully answer questions about computational thinking

Lesson structure

This lesson introduces you to the concept of computational thinking.

It includes, text, images, links to video and multiple choice questions.

Use the information, and any images from the presentation or elsewhere, to build up your own 'textbook' of learning using Book Creator on your iPad.

Read all the information carefully as there are a number of timed questions afterwards.

Introduction

Before computers can be used to solve a problem, the problem itself and the ways in which it could be resolved must be understood.

Computational thinking techniques help with these tasks.

What is computational thinking?

Computers can be used to help us solve problems. However, before a problem can be tackled, the problem itself and the ways in which it could be solved need to be understood.

Computational thinking allows us to do this.

Computational thinking allows us to take a complex problem, understand what the problem is and develop possible solutions. We can then present these solutions in a way that a computer, a human, or both, can understand.

The four cornerstones of computational thinking:

• 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

Each cornerstone is as important as the others. They are like legs on a table - if one leg is missing, the table will probably collapse.

Correctly applying all four techniques will help when programming a computer.

Computational thinking in practice

A complex problem is one that, at first glance, we don't know how to solve easily.

Computational thinking involves taking that complex problem and breaking it down into a series of small, more manageable problems (decomposition).

Each of these smaller problems can then be looked at individually, considering how similar problems have been solved previously (pattern recognition) and focusing only on the important details, while ignoring irrelevant information (abstraction).

Next, simple steps or rules to solve each of the smaller problems can be designed (algorithms).

Finally, these simple steps or rules are used to program a computer to help solve the complex problem in the best way.

Watch a video explanation:

Copy one of the web addresses into your web browser's address bar to view a video explanation of computational thinking:

https://www.youtube.com/watch?v=mUXo-S7gzds

https://www.youtube.com/watch?v=qbnTZCj0ugI

Thinking computationally

Thinking computationally is not programming. It is not even thinking like a computer, as computers do not, and cannot, think.

Simply put, programming tells a computer what to do and how to do it. 

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

For example, if you agree to meet your friends somewhere you have never been before, you would probably plan your route before you step out of your house.

You might consider the routes available and which route is ‘best’ - this might be the route that is the shortest, the quickest, or the one which goes past your favourite shop on the way.

You'd then follow the step-by-step directions to get there. In this case, the planning part is like computational thinking, and following the directions is like programming.

Being able to turn a complex problem into one we can easily understand is a skill that is extremely useful. In fact, it's a skill you already have and probably use every day.

For example, it might be that you need to decide what to do with your group of friends.

If all of you like different things, you would need to decide on a number of considerations (see next slide):

Consideration for activities with friends:

• what you could do 

• where you could go 

• who wants to do what

• what you have previously done that has been a success in the past

• how much money you have and the cost of any of the options

• what the weather might be doing

• how much time you have

From this information, you and your friends could decide more easily where to go and what to do – in order to keep most of your friends happy.

You could also use a computer to help you to collect and analyse the data to devise the best solution to the problem, both now and if it arose again in the future, if you wished.

Another example might occur when playing a videogame. Depending on the game, in order to complete a level you would need to know (see next slide):

Videogame considerations:

• what items you need to collect, how you can collect them, and how long you have in which to collect them

• where the exit is and the best route to reach it in the quickest time possible 

• what kinds of enemies there are and their weak points

Working out strategy

From these details you can work out a strategy for completing the level in the most efficient way.

If you were to create your own computer game, these are exactly the types of questions you would need to think about and answer before you were able to program your game.

Computational thinking has been used to solve a complex problem:

• each complex problem was broken down into several small decisions and steps (eg where to go, how to complete the level – decomposition)

• only the relevant details were focused on (eg weather, location of exit – abstraction)

• knowledge of previous similar problems was used (pattern recognition...

• ...to work out a step by step plan of action (algorithms)

Multiple Choice

What is computational thinking?

• Giving instructions to a computer

• Thinking like a computer - in binary

• Using a set of techniques and approaches to help to solve problems

Multiple Choice

Why do we need to think computationally?

• To help us to program

• To help us solve complex problems more easily

• To help us to think like a computer

Multiple Choice

Which of the following is NOT a computational thinking technique?

• Decomposition

• Pattern recognition

• Coding

Multiple Choice

Which of the following is an example of thinking computationally?

• Planning out your route when going to meet a friend

• When going to meet a friend, wandering around until you find them

• When going to meet a friend, asking a parent to plan your route for you

Multiple Choice

Which of the following is NOT an example of computational thinking?

• Planning what to collect and where to exit to complete a video game level

• Planning how to beat your enemies in a video game level

• Accidentally completing a video game level

Multiple Choice

Which of the following is NOT an example of computational thinking?

• Letting the bossiest friend decide where you should all go

• Considering the different options carefully before deciding upon the best one

• Discussing with your friends how much time and money you have before choosing from a shortlist of places

Multiple Choice

What is a complex problem?

• A problem that, at first, is not easy to solve

• A problem that, at first, is not easy to understand

• A problem that, at first, is not easy to solve or to understand

Multiple Choice

Which computational thinking technique involves breaking a problem down into smaller parts?

• Decomposition

• Abstraction

• Algorithms

Multiple Choice

To create a successful computer program, how many computational thinking techniques are usually required?

• Two

• Three

• Four

Multiple Choice

When is a computer most likely to be used when using computational thinking?

• During decomposition

• At the end, when programming a computer

• When writing algorithms

Poll

How well do you rate your understanding of what computational thinking is?

How well do you know what the four cornerstones or aspects of computational thinking are?

• Excellent

• Very good

• Good

• Satisfactory

• Poor