Reading Notes: What is computational thinking?

It represents a universally applicable attitude and skill set everyone, not just computer scientists, would be eager to learn and use.

CT builds on the power and limits of computing process:

1. What can humans do better than computers ?

2. What can computers do better than humans?

3. What is computable?

CT involves solving problems, designing systems, and understanding human behaviors.

1. How difficult is it to solve problems?

2. What is the best way to solve problems?

CT is using abstraction and decomposition when attacking a large complex taskor designing a large complex system.

1. Remind me the Tesla example I gave in Flipgrid video

2. Modeling and appropriate representation.

Gridlock deadlock and contracts interfaces. (? little bit confused on this at this point )

Thinking like a computer scientist means more than being able to program a computer. It requires thinking at multiple levels of abstraction.

CT is : Conceptualizing, not programming.Fundamental, not rote skill.A way that humans, not computers, think. Complements and combines mathematical and engineering thinking.Ideas, not artifacts. For everyone, everywhere.

Computer = human/machine

To a 3rd grader: instruct friends to bake cookies in a precise way.

How Wing’s thoughts changed on CT:

1. Not changed at all, still believe it . w/2 changes

2. The thought process that to formulate a problem and to express its solution : That could be more than one solution to a problem.

3. Embracing of this concept at the K through 12. {CT for all}

How CT distinguishes between other thinkings.

1. CS = mathematics as underlying foundation. (YAY!!! – from Shuaiqi, a math major student )

2. Overlap mathematical thinking – modeling, abstraction, logic(inductive and deductive reasoning )

3. Overlap engineering thinking – building with constraints

4. Distinction: software- you can do anything

Assess CT in classroom? Building more concepts

CT=thinking like a computer scientist

K-12: what are the right concepts to teach at right age?

What aspects of computers in education did Papert predict correctly over 40 years ago? Which ones missed the mark by today’s standards?

1. Children will engage in self-referential discussions about their own thinking

2. Computer can help in the teaching of traditional curriculum, but it is a Piagetian learning: Learn without curriculum.

Correct:

I predict that long before the end of the century, people will buy children toys with as much computer power as the great IBM computers currently selling for millions of dollars.

Work with a computer can be easier

Computer might produce some improvement in school learning

The cost of computers will fall to a level where they will enter everyday life in vast numbers.

The content might be varied to suit the tastes of each individual viewer and the show might become interactive, drawing the “viewer” in the action.

Miss:

the main cost of these machines will be the peripheral devices, such as the keyboard

The school learning is not likely to lead to fundamental change

How did Papert envision computers as “objects to think with” to support learning and problem solving in the classroom?

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In the computer programing, you almost never get it right first time. To be a master in programming is to correct bugs. It is not about right or wrong, but it is a question that if the situation is fixable. Students will become more persevere to solve problems, and to face challenges, because they will be less intimidated by their fears of being wrong.

Solving Problems at Google Using Computational Thinking

Problem decomposition

Pattern recognition

Algorithm design

Make pegman smarter 🙂 Use math to create something beautiful .

Curzon, P., & McOwan, P. W. (2017). The power of computational thinking: Games, magic and puzzles to help you become a computational thinker. World Scientific Publishing Company.

CT : a collection of diverse human skills to do with problem solving that result from studying the nature of computation.

1. A core ability everyone should learn.

2. Widely useful

3. Develop skills such as creativity, the ability to explain things clearly, and teamwork.

Algorithm thinking : core of computational thinking. Thinking of solutions to problems in different ways.

1. Gives solutions for whole group of problems

2. Could be able to do any calculations

Pattern matching(and generalization): the skill of reusing the old solution on new situation.

Abstraction: hide or ignore some of the details

Decomposition: split the question into smaller different parts

Generalization:way of quickly solving new problems based on previous problems we have solved.