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Is Computational Thinking Just Coding?-Pt. 2 #80

“Learning to use computers can change the way they (children) learn everything else.” Seymour Papert, Mathematician, Computer Scientist, Educator and Founder of MIT Media Lab

Hi, this is the GenWise team– we bring out this newsletter to help parents and educators to complement the work of formal schools and associated systems. We can help our children thrive in these complex times only by exchanging ideas and insights and working together. 

We are also a founder-member of the Gifted India Network– if you are interested in issues related to gifted education and talent development, an easy way to keep updated about talks, programs and resources is to join the Gifted India Network telegram channel (https://t.me/GiftedIndia).

Earlier in July, GenWise co-founder, Vishnu Agnihotri, participated in the Computational Thinking in Schools Conference and was part of a panel discussion titled ‘Computational Thinking and K-12 Education: The Way Forward’. As this topic is currently on our mind, we take the opportunity to share our thoughts on computational thinking across 2 posts (this and the previous one). The conference was organised by CSpathshala, a leader in computational thinking education in schools. Interested people can view the proceedings of the conference on the CSpathshala YouTube Channel. Mathematical and Computational Thinking is one of the 5 GenWise curricular tracks.

This week’s post explores the the relevance of Computational Thinking (CT) in learning other subjects, and the question of what CT should ‘every student’ know (not just the ones who want to pursue careers in Computer Science). Last week’s post emphasised how coding is just one element of developing Computational Thinking (CT) skills and the importance of using ‘unplugged’ activities in teaching CT skills.

Using CT as a Way of Learning

Examples of using CT in Learning

Consider 2 ways of learning about data distributions in middle/ high school-


The terms mean, median and mode are defined. Students are then given problems in which they calculate these descriptors for different datasets. They calculate these manually or use calculators.


Students are trained to work on spreadsheets like excel. They are given a list of formulas like average() and median(), and pointed to different spreadsheet functionalities- like options to plot data like scatter plots, piecharts etc., and conditional formatting They are now given salary data of 2 companies with 10 employees each. In company 1, the range of annual salaries is INR 3.3 lacs- 8.8 lacs, while in company 2, the range of annual salaries is INR 3.3 lacs- 30.0 lacs. Students are then asked to study patterns in these data and represent these in different ways. This leads students to do various things like sorting the data table, doing conditional formatting to visually understand the range of salaries, explore the values of mean and median, plot bar charts etc. (see examples of what students might do in the ‘playing with data’ tab of this sheet. After a couple of periods of working on this ‘project’, the teacher formally defines mean, median and mode and asks students which descriptor is best suited for which dataset and why.

It should be clear that method 2 will lead to much deeper learning than method 1. Is the difference between the 2 methods mainly because of the use of computers and spreadsheet software in method 2? Not really. An effective math teacher would have used a similar exploration of the data even if computers were not available, and a less competent math teacher would have given students mechanical tasks even when computers were available. But the availability of computers and software multiplies the possibilities open to both the teacher and the learner.

We had shared a few other examples in previous posts- examples of learning the laws of motion and the particulate nature of matter were shared in the post- Using Computer Simulations in Learning- 1, and a game theory example was shared in the post- Bhagavad Gita & a game theory simulation.

‘Constructionism’- the common thread in the examples

Mitchel Resnick, student of Seymour Papert and the inventor of Scratch, says in the foreword to the 2020 edition of Papert’s classic book, Mindstorms

Seymour argued that children construct knowledge most effectively when they are actively engaged in constructing things in the world. As children construct things in the world, they construct new ideas and theories in their minds, which motivates them to construct new things in the world, and on and on. Seymour saw rich learning opportunities in all different types of “construction” activities: building sand castles on the beach, writing stories in a diary, drawing pictures in a sketchbook. Why was Seymour so interested in computational technologies? Because he recognized that computational technologies can greatly expand the range of what and how children create. With computers, children can create things that move, interact, and change over time, such as animations, simulations, and interactive games.”

Papert foresaw these possibilities more than 40 years ago- before the internet, laptops and mobile phone!!

All the examples shared in the previous section have 2 common features-


They allow learners to actively engage in constructing things- one is learning by doing and creating– by sorting data, plotting it in different ways, by observing the distance a ball falls in each passing second, by studying the effects of temperature on the movement of molecules, by studying human interactions…


None of these activities is possible without the use of computers. This is what Resnick and Papert mean by ‘computational technologies can greatly expand the range of what and how children create’.

Constructionism is a term for the theory that children construct knowledge most effectively when they are actively engaged in constructing things in the world. And today’s world very much includes what you can construct on a computer…

Thus, in constructionism, projects play a critical role in learning, but these projects are quite different from the regular school project, as Resnick explains-

“(Seymour) believed that people learn to solve problems (and learn new concepts and strategies) most effectively while they are actively engaged in meaningful projects. Too often, schools start by teaching concepts to students, and only then give students a chance to work on projects. Seymour argued that it is best for children to learn new ideas through working on projects, not before working on projects.”

Listen to GenWise co-founder, Vishnu Agnihotri, speak here about the importance of constructionism, how there’s an element of ‘jugaad’ in CT and how he himself has not developed strong CT skills due to a lack of exposure to this way of thinking.

What CT skills should everyone should learn?

When we ask this question, what we mean by ‘everyone’ is students in school till a certain grade level (say grade 8) and all citizens. Take the case of Math- everyone needs to learn things like simple arithmetic, understand compound interest and basic geometry and measurement, they need not learn vector algebra, trigonometry and calculus unless they want to pursue certain fields. Similarly, educators need to understand what CT skills everyone should learn, not just those who will pursue computer science or even other STEM fields. The answer to the question of what ‘everyone should learn’ is not just based on whether they will ‘use it’ later in life- it is based on other considerations too. One important consideration is to provide students exposure to different ways of thinking and different disciplines- which allows them to develop their thinking skills and also to find their interests and recognise their aptitudes. Thus even young children in grade 4-5 may be exposed to somewhat complex CT ideas, even if everyone is not able to learn these easily. However it doesn’t make sense to force all students to learn these ideas beyond a certain stage, and therefore ‘what everyone should learn’ is an important question educators need to answer. This is the reason many examination boards offer 2 levels of a subject.

While education researchers have been talking about CT for a long time, this is still a fairly new topic of discussion among teachers. Our points below will hopefully contribute to a larger discussion that leads to a recognition of CT skills that everyone should learn. (Note how the below list doesn’t say that ‘everyone should code’, though everyone should appreciate broadly how code works)


The ability to write precise instructions (or recipes) is a life skill for planning and effective communication (e.g. a travel itinerary with a sequence of preparatory tasks)


The ability to organise data and use tools to make sense of the data. Awareness of the available tools is also important. The ability to think critically about data is not independent of the tools we use to work with data. Mr. Mukesh Kumar, Vice Principal and HOD- Computer Science, of Delhi Public School, RK Puram, New Delhi, shared a great example of this in a panel discussion at the Computational Thinking in Schools Conference. Mukesh spoke about how, when looking at the departure gates display at an airport, some people struggle to locate their flight for a long time because they don’t realise that the flights are sorted by departure time.


The ability to learn different things through computer simulations and other tools- creating, doing, debugging etc. as in the examples of the previous section.


In today’s world, people need to understand that everything (text, images, networks, user interfaces, whatever) can be represented as information which can be processed computationally. While everyone need not (and cannot) have the ability to represent things as data and process these data, an appreciation of this is important. This appreciation allows one to protect oneself from some of the dangers of the internet and AI world (misinformation and fraud). It also increases the capability of professionals to collaborate with others on solving problems- an engineer, a designer or a lawyer recognises how a problem lends itself to a computational approach.

Do let us know your thoughts about the points raised in this and the previous post by commenting below or writing to vishnu@genwise.in. We share some more resources below, in addition to those shared previously.

Recommended Resources on CT

The below resources are relevant to school principals, heads of departments and teachers.


‘Computational Thinking: The New Buzz’, by Prof. R. Ramanujam tries to define what computational thinking is (and is not) rigorously.


The video below is a talk by Prof. R. Ramanujam, that covers the same topic as in 1, but has more examples.


CSpathshala has created some excellent posters to dispel misconceptions around CT. If you are a teacher or a school who would like to get these posters, please contact them through the form on this page.

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