NEP: Computational Thinking

National Education Policy  Computational Thinking

What is algorithms? How does it translate in the NEP?

• The notions of calculation and algorithms are as old as mathematics and date back to the early stages of representing numbers and geometrical figures and manipulating them for various uses.
• All early learning of counting and arithmetic is method-based, and hence algorithmic in nature, and all calculations involve computational processes encoded in algorithms.
• The framing in the NEP appears to put algorithms at the same level as instrumental ‘coding’. It is considered as a towards the functional goals of artificial intelligence (AI) and data science.

How is coding and algorithm different?

• The form of expressions of algorithms and the coding have been different however the fundamental principles of classical algorithm design have remained same.
• The use of algorithmic ideas is not limited only to calculations with numbers, or even to digitisation, communication or AI and data science in the modern world. It is used in:
• They play a crucial role in modelling and expressing ideas in diverse areas of human thinking including the basic sciences of biology, physics and chemistry, all branches of engineering.
• In understanding disease spread.
• In modelling social interactions and social graphs.
• In transportation networks, supply chains, commerce, banking and other business processes.
• In economic and political strategies and design of social processes.
• Coding is merely the act of encoding an algorithmic method in a particular programming language, which provides an interface such that the computational process can be raised in a modern digital computer. Thus, it is less fundamental.
• Coding certainly can provide excellent opportunities for experimentation with algorithmic ideas, they are not central or essential to algorithmic thinking.

Why is it important to learn the fundamentals?

• It is important at an early stage of education to develop an understanding of the basic algorithmic processes behind manipulating geometric figures, computing with numbers, solving systems of equations, modelling road networks and social graphs, and applying algorithmic ideas to everyday problems.
• An overemphasis on learning of specific programming languages prematurely may distract from focusing on the development of algorithmic creativity.

Way forward

• While the NEP guideline of introducing algorithmic thinking early is a welcome step, it must be ensured that it does not degenerate and get bogged down with mundane coding tricks at a budding stage in the education process.