Introduction to Computational Thinking and Design Thinking, Schemes and Mind Maps of Java Programming

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Computational thinking

Problem solving and

programming

Lecture 1

Introduction to computational thinking

Computational Thinking (CT)

q

CT

Computational thinking (CT) is a

problem-solving approach using

computer, that formulates a problem

and its solution to be effectively

executed by a computer. (Wing 2014 ).

q CT is a problem-solving approach

where the ultimate aim is to provide a

solution whose form is ready to be

programmed into a computer.

How is computational thinking used?

q Anyone can apply CT when solving a problem and

have a computer play a role in the solution:

§ Mathematician: carry out long division factoring

or doing carries in addition or subtraction.

§ Scientist: do an experimental procedure.

Design thinking model

7

Some key advantages of developing computational thinking skills:

1. Problem-solving skills: CT equips individuals with a structured

approach to problem-solving. It emphasizes breaking down

complex problems into smaller, more manageable parts,

identifying patterns, and designing algorithms to solve them.

2.Logical reasoning: CT promotes logical reasoning by encouraging

individuals to analyze problems, identify cause-and-effect

relationships.

3.Creativity and innovation : CT encourages individuals to think

creatively and come up with innovative solutions.

Computational thinking pillars (skillls)

qThese characteristics will help you to think computationally

through a complex problem:

Decomposition

Abstraction

Pattern recognition

Algorithmic thinking

logical reasoning

Decomposition (Example)

Look at an example, the

equation to find the roots

of a quadratic equation:

Decomposition (Example)

q On first look, it might appear a little scary, but if we

decompose it, we should stand a better chance of solving it:

1. b

2

2. 4ac

3. b

2

• 4ac

4. b

2

− 4ac

5. • b ∓ b

2

− 4ac

6. 2a

7. x=

−b! b

2

− 4ac

"#

8. x=

−b − b

2

− 4ac

"#

2. Abstraction

q Abstraction is about reducing the

complexity of a problem or task by

focusing on what is important and

removing unnecessary details.

q It can also be used to have one object

stand for many, or to have a word stand

for an action. Models and simulations

can also be considered abstractions.

Abstraction (Example)

Find the

important

skills for a

baseball

pitcher.

Represent the

actions run,

stop and hop

using images.

Code a

simulation of a

volcano

erupting.

Find the

appropriate

formula to solve

a physics

problem.

Pattern recognition (Example)

4. Algorithmic thinking

q Algorithm is a series of ordered and

unambiguous instructions to solve a problem.

q Computer scientists seeks to find an

effective and efficient algorithm to solve a

problem with minimum computing resources

(memory or time) while getting the correct

output.