Computational thinking, problem-solving and programming: Difference between revisions

From Computer Science Wiki
Line 43: Line 43:
=== Programming Design ===
=== Programming Design ===


4.2.1 Describe the characteristics of standard algorithms on linear arrays. Level: 2
* [[Linear arrays]]
4.2.2 Outline the standard operations of collections. Level: 2
* [[Collections]]
4.2.3 Discuss an algorithm to solve a specific problem. Level: 3
* [[Pseudocode]]
4.2.4 Analyse an algorithm presented as a flow chart. Level: 3
* [[Algorithms]]
4.2.5 Analyse an algorithm presented as pseudocode. Level: 3
* [[Applied algorithmic thinking]]
4.2.6 Construct pseudocode to represent an algorithm. Level: 3
4.2.7 Suggest suitable algorithms to solve a specific problem. Level: 3
4.2.8 Deduce the efficiency of an algorithm in the context of its use. Level: 3
4.2.9 Determine the number of times a step in an algorithm will be performed for given input data. Level: 3


=== Introduction to Programming ===
=== Introduction to Programming ===

Revision as of 15:23, 12 April 2016

Exclamation.png This is one of the most important ideas you can take with you:

Decompose a problem into smaller parts, model a problem with flowcharts. Learn to think sequentially

Computational thinking, problem-solving and programming[1]

Computational Thinking (CT) is a process that generalizes a solution to open-ended problems. Open-ended problems encourage full, meaningful answers based on multiple variables, which require using decomposition, data representation, generalization, modeling, and algorithms found in Computational Thinking. Computational Thinking requires the decomposition of the entire decision making process, the variables involved, and all possible solutions, ensuring that the right decision is made based on the corresponding parameters and limitations of the problem. The term computational thinking was first used by Seymour Papert in 1980[1] and again in 1996.[2] Computational thinking can be used to algorithmically solve complicated problems of scale, and is often used to realize large improvements in efficiency[2]


The big ideas in computational thinking[edit]

Thinking procedurally[edit]

Thinking logically[edit]

Thinking ahead[edit]

Thinking concurrently[edit]

Thinking abstractly[edit]

Programming Design[edit]

Introduction to Programming[edit]

Use of programming languages[edit]

References[edit]