Computational Thinking

The concept of computational thinking was first introduced by Professor Seymour Papert of the Massachusetts Institute of Technology (MIT) in 1996[1]. It represents a mindset for solving and analyzing problems, helping individuals gain a deeper understanding of the essence and core ideas of various issues. As a result, computational thinking has found widespread application across various industries today. It embodies a systematic approach to problem-solving, involving the skill of breaking down complex problems into manageable components.

Turing Award laureate Karp has asserted[2] that both natural and societal issues inherently contain rich computational patterns, evolving alongside transformations in matter, energy, and information. Thus, the correct extraction of these information transformations and their expression through computational thinking facilitates their more straightforward handling by computers or individuals. This forms the methodology for solving computational and even societal problems based on the concept of computational thinking.

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Computational thinking encompasses several key concepts that play pivotal roles in future learning and professional careers:

Decomposition:This concept teaches us to decompose complex problems into smaller, more manageable sub-problems, thus rendering the resolution of larger issues more attainable. It helps us work more efficiently in programming and problem-solving.

Abstraction:Abstraction demands that we strip away unnecessary details from a problem, honing in on its essential characteristics. This thought process simplifies problems, rendering them more accessible for comprehension and resolution. Within the realm of computational thinking, abstraction serves as a 'cognitive' tool, often more intricate than abstractions in other fields.

Pattern Recognition :Pattern recognition refers to the process of identifying similar patterns and commonalities in different objects, problems, or systems. By recognizing these patterns, individuals can leverage past experiences and outcomes to solve new problems.

Algorithm:Algorithm is a set of precise, ordered rules and steps created to address specific problems or perform particular tasks. It is a core concept in computing that is not only useful in data analysis but also plays a crucial role in problem-solving and discovery. Computational thinking places a strong emphasis on the core concept of 'computational complexity,' highlighting the importance of efficiency.

Richness of Abstraction.:In computational thinking, abstraction is not limited to neat, mathematical abstractions; it can be even richer and more complex. It requires consideration of the constraints of the physical world, edge cases, and potential failure scenarios. This makes abstractions in computational thinking distinct from those in mathematics and physics.

(The above computational thinking mind map Image source from

Overall, computational thinking encompasses these core concepts, which are not only crucial for programming and problem-solving but also play a pivotal role in future learning modules and careers. They cultivate innovative thinking and adaptability, empowering us to thrive in an increasingly complex technological and informational landscape.

Seymour Papert. An Exploration in the Space of Mathematics Educations[J]. International Journal of Computers for Mathematical Learning, 1996, Vol.1, No.1: 95-123.

Karp R. M. Understanding science through the computational lens[J]. Journal of Computer Science and Technology, 2011, 26(4): 569-577