Computational Thinking for Problem Solving

Start Date: 11/05/2018

Course Type: Common Course

Course Link: https://www.coursera.org/learn/computational-thinking-problem-solving

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About Course

Computational thinking is the process of approaching a problem in a systematic manner and creating and expressing a solution such that it can be carried out by a computer. But you don't need to be a computer scientist to think like a computer scientist! In fact, we encourage students from any field of study to take this course. Many quantitative and data-centric problems can be solved using computational thinking and an understanding of computational thinking will give you a foundation for solving problems that have real-world, social impact. In this course, you will learn about the pillars of computational thinking, how computer scientists develop and analyze algorithms, and how solutions can be realized on a computer using the Python programming language. By the end of the course, you will be able to develop an algorithm and express it to the computer by writing a simple Python program. This course will introduce you to people from diverse professions who use computational thinking to solve problems. You will engage with a unique community of analytical thinkers and be encouraged to consider how you can make a positive social impact through computational thinking.

Course Syllabus

Computational thinking is an approach to solving problems using concepts and ideas from computer science, and expressing solutions to those problems so that they can be run on a computer. As computing becomes more and more prevalent in all aspects of modern society -- not just in software development and engineering, but in business, the humanities, and even everyday life -- understanding how to use computational thinking to solve real-world problems is a key skill in the 21st century. Computational thinking is built on four pillars: decomposition, pattern recognition, data representation and abstraction, and algorithms. This module introduces you to the four pillars of computational thinking and shows how they can be applied as part of the problem solving process.

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Course Introduction

Computational thinking is the process of approaching a problem in a systematic manner and creating a

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Article Example
Computational thinking Jeannette Wing envisioned computational thinking becoming an essential part of every child's education. However, since her article (published in 2006) integrating computational thinking into the K-12 curriculum has faced several challenges including the agreement on the definition of computational thinking. Currently Computational Thinking is broadly defined as a set of cognitive skills and problem solving processes that include (but are not limited to) the following characteristics:
Computational thinking Current integration computational thinking into the K-12 curriculum comes in two forms: in computer science classes directly or through the use and measure of computational thinking techniques in other subjects. Teachers in Science, Technology, Engineering, and Mathematics (STEM) focused classrooms that include computational thinking, allow students to practice problem-solving skills such as trial and error (Barr, et al, 2011). Valerie Barr and Chris Stephenson describe computational thinking patterns across disciplines in a 2011 ACM Inroads article However Conrad Wolfram has argued that computational thinking should be taught as a distinct subject.
Computational thinking Computational Thinking (CT) is the thought processes involved in formulating a problem and expressing its solution(s) in such a way that a computer—human or machine—can effectively carry out. Computational Thinking is an iterative process based on three stages: 1) Problem Formulation (abstraction), 2) Solution Expression (automation), and 3) Solution Execution & Evaluation (analyses) captured by the figure to the right. The term "computational thinking" was first used by Seymour Papert in 1980 and again in 1996. Computational thinking can be used to algorithmically solve complicated problems of scale, and is often used to realize large improvements in efficiency.
Computational problem is a computational problem. Computational problems are one of the main objects of study in theoretical computer science. The field of algorithms studies methods of solving computational problems efficiently. The complementary field of computational complexity attempts to explain why certain computational problems are intractable for computers.
Stochastic thinking Stochastic thinking for problem solving proceeds in three steps:
Computational thinking Carnegie Mellon University in Pittsburgh has a Center for Computational Thinking. The Center's major activity is conducting PROBEs or PROBlem-oriented Explorations. These PROBEs are experiments that apply novel computing concepts to problems to show the value of computational thinking. A PROBE experiment is generally a collaboration between a computer scientist and an expert in the field to be studied. The experiment typically runs for a year. In general, a PROBE will seek to find a solution for a broadly applicable problem and avoid narrowly focused issues. Some examples of PROBE experiments are optimal kidney transplant logistics and how to create drugs that do not breed drug-resistant viruses.
Problem solving Problem solving is applied on many different levels − from the individual to the civilizational. Collective problem solving refers to problem solving performed collectively.
Computational thinking The concept of Computational Thinking has been criticized as too vague, as it's rarely made clear how it is different from other forms of thought. Some computer scientists worry about the promotion of Computational Thinking as a substitute for a broader computer science education, as computational thinking represents just one small part of the field. Others worry that the emphasis on Computational Thinking encourages computer scientists to think too narrowly about the problems they can solve, thus avoiding the social, ethical and environmental implications of the technology they create.
Problem solving Problem solving consists of using generic or "ad hoc" methods, in an orderly manner, for finding solutions to problems. Some of the problem-solving techniques developed and used in artificial intelligence, computer science, engineering, mathematics, or medicine are related to mental problem-solving techniques studied in psychology.
Computational thinking The phrase "computational thinking" was brought to the forefront of the computer science community as a result of an ACM Communications article on the subject by Jeannette Wing. The article suggested that thinking computationally was a fundamental skill for everyone, not just computer scientists, and argued for the importance of integrating computational ideas into other disciplines.
Problem solving Considered the most complex of all intellectual functions, problem solving has been defined as a higher-order cognitive process that requires the modulation and control of more routine or fundamental skills. Problem solving has two major domains: mathematical problem solving and personal problem solving where, in the second, some difficulty or barrier is encountered.
Problem solving In psychology, problem solving refers to a state of desire for reaching a definite 'goal' from a present condition that either is not directly moving toward the goal, is far from it, or needs more complex logic for finding a missing description of conditions or steps toward the goal. It is the evolutionary drive for living organisms. The nature of human problem solving processes and methods has been studied by psychologists over the past hundred years. Methods of studying problem solving include introspection, behaviorism, simulation, computer modeling, and experiment. Social psychologists also look into the independent and interdependent problem-solving methods. In psychology, problem solving is the concluding part of a larger process that also includes problem finding and problem shaping.
Computational problem A decision problem is a computational problem where the answer for every instance is either yes or no. An example of a decision problem is "primality testing":
Computational thinking The characteristics that define computational thinking are decomposition, pattern recognition / data representation, generalization/abstraction, and algorithms. By decomposing a problem, identifying the variables involved using data representation, and creating algorithms, a generic solution results. The generic solution is a generalization or abstraction that can be used to solve a multitude of variations of the initial problem.
Future Problem Solving Program International Future Problem Solving Program International- (FPSPI), formerly known as the Future Problem Solving Program (FPSP), aims to "engage students in creative problem solving". Founded by Dr. Ellis Paul Torrance in 1974, FPSPI was created to stimulate critical and creative thinking skills and to encourage students to develop a vision for the future. FPSPI features curricular and co-curricular competitive, as well as non-competitive, activities in creative problem solving. The Future Problem Solving Program International involves over 250,000 students annually from Australia, Canada, Hong Kong, Japan, Korea, Malaysia, Portugal, New Zealand, Russia, Singapore, Great Britain, Turkey, India and the United States.
Problem solving In cognitive sciences, researchers' realization that problem-solving processes differ across knowledge domains and across levels of expertise (e.g. Sternberg, 1995) and that, consequently, findings obtained in the laboratory cannot necessarily generalize to problem-solving situations outside the laboratory, has led to an emphasis on real-world problem solving since the 1990s. This emphasis has been expressed quite differently in North America and Europe, however. Whereas North American research has typically concentrated on studying problem solving in separate, natural knowledge domains, much of the European research has focused on novel, complex problems, and has been performed with computerized scenarios (see Funke, 1991, for an overview).
Problem solving environment A problem solving environment (PSE) is a completed, integrated and specialised computer software for solving one class of problems, combining automated problem-solving methods with human-oriented tools for guiding the problem resolution. A PSE may also assist users in formulating problem resolution. A PSE may also assist users in formulating problems, selecting algorithm, simulating numerical value and viewing and analysing results.
Problem solving The following techniques are usually called "problem-solving strategies'
Computational thinking There are a handful of online institutions which provide curriculum, and other related resources to build and strengthen pre-college students with Computational Thinking, Analysis and Problems Solving. One prominent one is the Carnegie Mellon Robotics Academy. It offers training sessions for both pre-college students, as well as teachers. CMU's programs exercise instructional scaffolding methods via engineering process. There is also another online site named legoengineering.com. offering similar resources.
Problem solving The term "problem solving" is used in many disciplines, sometimes with different perspectives, and often with different terminologies. For instance, it is a mental process in psychology and a computerized process in computer science. Problems can also be classified into two different types (ill-defined and well-defined) from which appropriate solutions are to be made. Ill-defined problems are those that do not have clear goals, solution paths, or expected solution. Well-defined problems have specific goals, clearly defined solution paths, and clear expected solutions. These problems also allow for more initial planning than ill-defined problems. Being able to solve problems sometimes involves dealing with pragmatics (logic) and semantics (interpretation of the problem). The ability to understand what the goal of the problem is and what rules could be applied represent the key to solving the problem. Sometimes the problem requires some abstract thinking and coming up with a creative solution.