The Science of Learning:


“ The Science of Learning”

Structure:

1: The Backdrop

2: Development of the Sciences

3: Educational practice ignores research

4: Bloom’s 2 sigma problem

5: What machine learning informs us about human learning?

6: The current state of assessment

7:  AInEd Supremacy 

1: The Backdrop

I have just been hearing all the noise about the notifications and preparations for the CBSE and other Board examinations. Students aged 16 to 18 years, would be wasting their next 6 months in a completely detrimental activity. Some will also be spending substantial time and money with coaching Institutes doing more of the same.

In a recent (2018) book “ The fourth Education Revolution”, its author Sir Anthony Seldon lists (pages 165 and 197) the negative attributes of these monolithic examinations and celebrates their imminent death. This is not the only example of the divergence of educational practice from what research says.

To begin with, the learning pyramid ( https://www.educationcorner.com/the-learning-pyramid.html) suggests that most students only remember about 5% of what they hear in lectures, 10% of what they read from textbooks, 50% of what they discuss but retain nearly 90% of what they learn through teaching others. Yet our dominant form of teaching is lectures. The CBSE prescribes the mandatory  number of these mostly ineffective lectures, which must be attended before being allowed to take examinations. Shouldn’t we be appreciating more those who can learn without attending lectures. 

It shouldn’t surprise us that most School leavers are not ready either for the world or for further education, and are ready to be “the useless class” of the 21st Century described by Yuval Noah Harari ( https://ideas.ted.com/the-rise-of-the-useless-class/). 

The practice of medicine progresses because the outcome of medical research is applied to medical practice. We saw last year that medical science made a big jump in understanding and then making products with RNA vaccines for the first time in record time while dealing with the Coronavirus vaccine. But in the corresponding urgent matter of making our youth future ready and relevant, the policy making is driven in a leisurely mode, with no reference to the Science of learning, sometimes in complete opposition to what learning science tells us. 

Stanford University Professor Savas Dimopolous says  “ What differentiates scientists is an intrinsic ability to discern a good idea, a beautiful idea, worth spending time on and most importantly what is a problem that is sufficiently interesting and difficult that it hasn’t been solved yet and now is  the time for solving it “. The Science of Learning is such an emerging field. 

The Science of Learning is building on the advances in neurosciences and the availability of large amounts of data, which drove learning analytics and educational data mining. Now with Machine learning and deep learning helping us understand how machines learn, there can be new research and applications in the science of learning. Just as advances in Physiology/ medicine as well in Physics and Chemistry spurred the development of the Pharmaceutical Industry, we will see the development of a whole new educational ecosystem in the coming decades. An educational diagnostics industry will replace the erstwhile School leaving Board and end of year University examinations. This has been very well articulated in Anthony Seldon’s book “ The fourth Industrial Revolution” at p.165 and 197 referred to earlier in this piece. 

There are connections between, learning, computing and brain science which are well illustrated in this TED talk by Jeff Hawkins: 

Another closely connected idea to human learning is consciousness. Roger Penrose on Quantum Physics of Consciousness : https://youtu.be/43vuOpJY46s

About a hundred years ago, Quantum Mechanics developed with a number of persons attempting to overcome the limitations of classical Physics, and now we are witnessing the 2nd Quantum Revolution. Similarly developments in the Sciencr of Learning will drive the 4th Education Revolution.

2: Development of the Sciences

In  prehistoric times, knowledge and technique were passed from generation to generation in an oral tradition. For instance, the domestication of maize for agriculture has been dated to about 9,000 years ago in southern Mexico, before the development of writing systems. Similarly, archaeological evidence indicates the development of astronomical knowledge in preliterate societies.. The development of writing enabled humans to store and communicate knowledge across generations with much greater accuracy.

If we fast forward to the early 19th century, this was a period that shaped science. Major breakthroughs came in biology, especially in Darwin’s theory of evolution as well as physics (electromagnetism), mathematics (non-Euclidean geometry, group theory) and chemistry (organic chemistry).

paradigm shift, a concept identified by the American physicist and philosopher Thomas Kuhn,  is a fundamental change in the basic concepts and experimental practices of a scientific discipline. Even though Kuhn restricted the use of the term to the natural sciences, the concept of a paradigm shift has also been used in numerous non-scientific contexts to describe a profound change in a fundamental model or perception of events.

Kuhn presented his notion of a paradigm shift in his influential book “ The structure of Scientific revolutions” published in 1962. 

Kuhn contrasts paradigm shifts, which characterize a scientific revolution to the activity of normal science,which he describes as scientific work done within a prevailing framework or paradigm. Paradigm shifts arise when the dominant paradigm under which normal science operates is rendered incompatible with new phenomena, facilitating the adoption of a new theory or paradigm. This is what happened in the first 3 decades of the 20th Century with the development of Quantum Mechanics. A hundred years later we are witnessing the arrival of Quantum Technologies. 

In my view “ the science of learning” is right now in the pre-paradigm shift stage, and the coming 3rd decade of the 21st Century will see a new phase of “the science of learning”, much like the Copernican revolution. Or like Quantum Mechanics and Classical Physics. The current practice of education is driven by intuitively obvious but incorrect widely held beliefs, and it is now time to upgrade it by adopting research findings and evidence driven practices. 

3: Educational  practice  ignores research

I have made this comparison often. Till about a hundred years back, medical practice was like the present education system, disconnected from the science of Physiology. In his book, “ The Youngest Science” Thomas Lewis shares how he first learned about medicine by watching his father practice in an era when doctors comforted rather than healed. Looking back upon his experiences as a medical student, young doctor, and senior researcher, Thomas notes that medicine is now rich in possibility and promise. The medical fraternity adopted both science and technology, and integrated it so well that when the Cornavirus pandemic came, most people were surprised that the medical establishment had no treatment for it. This amazing progress in medicine has been achieved because the outcomes of research in medicine and Physiology are applied to medical practice.

In the practice of education, on the other hand, the knowledge from research and experiments is not only ignored, educational practices and policies including the NEP 2020, do just the opposite. 

The “learning pyramid”, sometimes referred to as the “cone of learning”, developed by the National Training Laboratory, suggests that most students only remember about 10% of what they read from textbooks, but retain nearly 90% of what they learn through teaching others. The Learning Pyramid model suggests that some methods of study are more effective than others and that varying study methods will lead to deeper learning and longer-term retention.

The Learning Pyramid suggests that “Lecture” is one of the most ineffective methods for learning and retaining information. Lecture is a passive form of learning where you simply sit back and listen to information being spoon fed to you by your teacher or professor. Attempting to acquire information and gain understanding only through lectures is not the most effective way of learning. Its effectiveness is only 5%. Audio-visuals have an effectiveness of 20%, demonstrations30%, discussions 50% and practice at doing is 70% effective. 

But teaching others is 90% effective. This is what the Physics Nobel 

Laureate Richard Feynman also advised. He recommended  the following  steps:

1: Write the name of the concept at the top of a blank piece of paper.

2: Write down an explanation of the concept on the page. Use plain English. Pretend you are teaching it to someone else (e.g a new student). This should highlight what you understand, but more importantly pinpoint what you don’t quite know.

3: Review what you have pinpointed you don’t know. Go back to the source material, re-read, and re-learn it. Repeat Step 2.

4: If you are using overly wordy or confusing language (or simply paraphrasing the source material) try again so you filter the content. Simplify your language, and where possible use simple analogy.

But the present education systems emphasises attending lectures, to the extent that at both School and at higher education levels there is a mandatory requirement to attend 75% of these rather ineffective lectures, before they can take the terminal examinations. The other more effective ways of learning are seldom used. Even the duration of the lectures, is several multiples of the attention spans of the learners, as indicated by research. There is sufficient educational research to suggest that ’chunking’ of learning is more effective than long duration lectures. 

Content should therefore be in the form of ‘learning objects’ that are re-usable and available as a repository of re-usable learning objects. Preference should be to nano-learning objects that can be transacted or perused in 1,2,5 or 10 minutes, and any learning episode may be built by putting together these learning objects according to the pedagogical requirements. Each learning object will have meta-data to allow automation in their search, retrieval and combination. 

4: Bloom’s 2 sigma problem:

In the early 1980s, renowned educational psychologist Benjamin Bloom made a breakthrough discovery. Examining teaching and learning techniques, he found a way to drastically improve student performance, helping average students perform better than 98 per cent of their “traditional classroom” contemporaries.

Having discovered a way to improve education efficiency by several orders of magnitude, he could deliver results for students that far outstripped the standards expected of them. However, Bloom also faced a significant challenge – his technique wasn’t scalable.

In 1984 Benjamin Bloom discovered a method for drastically improving educational efficiency, delivering results that are improved by a factor of two standard deviations (two sigma). His method would mean that the “average” student within a given class could now perform better than 49 out of every 50 students within a traditional classroom setting.

Bloom’s secret sauce? The combination of two education approaches: mastery learning and one-to-one tutoring.

  • Mastery learning: Each student must achieve true mastery of a topic before moving on to the next, more advanced subject. The student is given the time to study the topic until they succeed in achieving this mastery, even if that takes longer than other students. Time is the one constant in a traditional classroom setting, with lessons squeezed into set periods, leaving no room for different learning rates or individual student needs. With a focus on mastery learning, achievement becomes the constant.
  • One-on-one tutoring: Each student is provided with a personal tutor who guides them through their learning, suggesting specific exercises and unlocking the individual student’s potential on an ongoing basis and ensuring they truly “get” the subject.

Given the impact of Bloom’s discovery, one might have expected these techniques to be applied everywhere by now, but this just isn’t the case. Why?

One underlying reason is the resistance to change that can be seen throughout the academic world. Bloom’s new approach to teaching was significantly different to the system in place, meaning that it was naturally met with scepticism and reluctance.

But more important is the time and resource-intensive nature of Bloom’s method. As an approach that’s new to the majority of teachers, substantial time would be needed to set up a mastery-oriented teaching framework, and the move away from hour-long teaching blocks would be hugely disruptive to the traditional learning environment.

Plus, the requirement for one-to-one tutoring would be time and cost-intensive, and incredibly difficult to implement for large groups of students.

To circumvent this, Bloom tried to find other ways to produce his Two Sigma effect, but to no avail. Nothing was as effective as a combination of mastery learning and one-on-one tutoring. The challenge of scalability explains why Bloom’s approach to learning development has not been widely adopted, but that is set to change.

5: What machine learning informs us about human learning?

There are 3 main approaches to training machine learning models: supervised, unsupervised and reinforcement learning. The recent success of AlphaGo zero that learnt to play the game of Go, and defeated the human world champion, without ever being taught how to play the game, shows a lot of promise for self-learning, and developing the skill of learning how to learn. This is also a matter, emphasised in the NEP 2020. 

All human learning is — observing something, identifying a pattern, building a theory (model) to explain this pattern and testing this theory to check if its fits in most or all observations.

Overfitting refers to the scenario where a machine learning model can’t generalize or fit well on unseen dataset. Overfitting happens when a model learns the detail and noise in the training dataset to the extent that it negatively impacts the performance of the model on a new dataset.

The opposite of overfitting is underfitting. In human learning overfitting is demonstrated when a learner can do well only on certain types of questions for which s(he) has been very well trained but can’t respond to questions that elicit a broad understanding of the subject. 

6: The current state of assessment:

Of the various formats of questions that are used for assessments, multiple choice questions, fill in the blank or match entries from 2 columns are easily capable of being automated and evaluated with computers. Not only can a large number of students be graded automatically, the items can be evaluated on features such as facility, reliability and discrimination index. With good quality items for the bank of questions, it is relatively straightforward to implement an adaptive testing system.

The essay type questions are a formidable challenge to automated machine testing. But recent developments in machine learning, have made grading and assessments of essay type questions possible. 

This is also a time that shows up the irrelevance of the monolithic terminal examinations. Sir Anthony Seldon in his 2018 book “ The fourth education revolution” has analysed why such examinations will no longer be relevant. Sir Anthony, Vice-Chancellor of the University of Buckingham, added that continuous assessment would sound the death-knell for exams: “The all-conquering cumulative exam is going to die and we should celebrate its death…The monolithic exam is drawing to a spluttering end.”

Micro-credentials are one of the hot rising idea in the education space. To understand the basics, go look at your child’s Xbox or PlayStation.

For most of the major games, there is an accompanying set of achievements, or badges. Every time a player achieves a particular task (kill 50 zombies without reloading, drive over every tree in the enchanted forest, smash every Lego fire hydrant, etc.) they get a small digital badge on their big page of achievements.

Micro-credentials take a similar approach to education. The root of the idea is simple–you demonstrate a very specific skill, and a badge certifying that micro-credential becomes part of your personal digital file. Some of the earliest micro-credentialing involved computer programming skills, but it has grown far beyond that. To see just how many types of micro-credentials are out there, take a look at Digital Promise. 

It offers micro-credentials of its own, but it also provides a platform for other entities to offer their own sets of micro-credentials.

7: AInEd Supremacy: Recently China announced that it had achieved Quantum Supremacy. Google had made the same claim in October2019. The phrase ‘ Quantum Supremacy’  was used in 2012 by the Caltech Professor John Preskill to describe the  situation when a Quantum Computer could solve a problem that no classical computer of the day could solve in a reasonable time. 

In a similar spirit I have made a phrase “ AInEd  Supremacy” to denote the situation when the AI technology of the day in conjunction with a human educator will achieve learning outcomes that no traditional teacher with traditional technology could achieve. We may achieve AInEd Supremacy in about 5 years. 

The 2018 book by Anthony Seldon “ The fourth Education revolution” explains how AI will fundamentally transform education. He says that so far education has been the Cinderella of the AI story. 

Terrence Sejnowski, Francis Crick Professor at the Salk Institute for Biological Studies and Director of the Computational Neurobiology Laboratory, says that “ Education is going to be the killer app for deep learning”. 

Sir Anthony Seldon illustrates how each of five traditional factors in teaching will be transformed by AI over the coming decades: 

  1. Preparation of material will be done by ‘Curation specialists . . whose job it is to work with AI machines to author and identify the most appropriate material for particular student profiles.’ 
  2. Organisation of the learning space: ‘Separate classrooms will disappear in time and replaced by pods and wide open, flexible spaces which can be configured for individual and flexible collective learning. Sensors will monitor individual students, measuring their physiological and psychological state, picking up on changes faster and more accurately than any teacher could.’ 
  3. Presentation of material to optimise learning/deeper understanding: ‘The flexibility of visual representation with AI allows material to be presented to students which renders much teacher exposition redundant.’ 
  4. Setting assignments and assessing/self-assessing progress: ‘Advances in real-time assessment enabled by AI will virtually eliminate this waiting period [the time lag between students being assessed and them receiving feedback on their performance} and ensure feedback comes when most useful for learning.’ 
  5. Preparation for terminal examinations and writing summative reports: ‘All this will be swept away by AI. . . . In its place will be attention to continuous data reporting, and real time feedback that will help students discover how to learn autonomously and how to address any deficiencies on their own.’ 

Will we need teachers in the future? Seldon is clear ‘We do not believe that it is either possible or desirable for AI to eliminate teachers from education’ but he goes on to point out that ‘the application of AI places more responsibility for learning in the hands of the student, for how their time is spent and on what, even from a young age.’ AI will change however the job of the teacher forever. By supporting teaching in all their five traditional tasks, AI will usher in the biggest change the profession has ever seen.’ Interestingly Seldon recognises that remote teaching is a distinct possibility: ‘Imminent advances in virtual technologies will mean too that teachers no longer have to be physically present to offer their services.’ 

In addition to the help that AI will provide to facilitate the tasks of a teacher, there are a few more results that learning analytics will provide that enhance the teaching quality and the learning effectiveness. For example when the learner is transacting the content, data will automatically be recorded for the time spent at various sections. With permission granted to record facial expressions, there will be very useful data that indicates the level and nature of engagement with the content. The qualities of grit, perseverance etc. referred to by Paul Tough in his seminal work “ How children succeed” can now be observed and analysed quantitatively. Drawing on groundbreaking research in neuroscience, economics, and psychology, Tough shows that the qualities that matter most have less to do with IQ and more to do with character: skills like grit, curiosity, conscientiousness, and optimism.

To help with his class the spring of 2016, a Georgia Tech professor Ashok Goel, hired Jill Watson, a teaching assistant unlike any other in the world. Throughout the semester, she answered questions online for students, relieving the professor’s overworked teaching staff.

But, in fact, Jill Watson was an artificial intelligence bot.

Ashok Goel, a computer science professor, did not reveal Watson’s true identity to students until after they’d turned in their final exams.

With more human-like interaction, Goel expects online learning could become more appealing to students and lead to better educational outcomes.

Just as professors may use AI as teaching assistants, students could use a chatbot to facilitate individual mastery learning, the technology that Bloom suggested in his 1984 research….the two sigma  problem. It would have taken about 40 years to realise Bloom’s vision. 

In about a decade from now, the impact of the science of learning empowered with Artificial Intelligence will be seen on many features of the present educational landscape that we take for granted. Every student would have access to the best education in the field of choice, in a time schedule that is optimised for the learner. The various School leaving Board examining bodies will become irrelevant, as will the entrance examinations such as the various incarnations of the JEE-IIT, NEET, CAT, LSAT etc. Just like the technologies of CAT-scan, MRI or PET did not replace doctors, but made them more effective, the science of learning with technologies of the 4th Industrial Age will help educators to be a very important element of the success of the country and its people in the emerging knowledge economy. 

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Developing a learning culture:

Developing a learning culture :

A culture of learning, or learning culture, is one in which members of the group continuously seek, share, and apply new knowledge and skills to improve their individual and collective performance. The importance of the pursuit and application of learning is expressed in organizational values and permeates all aspects of organizational life.

Peter Senge, who popularized the concept of learning organizations in his book ‘The Fifth Discipline’, described them as places “where people continually expand their capacity to create the results they truly desire, where new and expansive patterns of thinking are nurtured, where collective aspiration is set free, and where people are continually learning how to learn together.” To achieve these ends, Senge suggested the use of five “component technologies”: systems thinking, personal mastery, mental models, shared vision, and team learning. In a similar spirit, Ikujiro Nonaka characterized knowledge-creating companies as places where “inventing new knowledge is not a specialized activity…it is a way of behaving, indeed, a way of being, in which everyone is a knowledge worker.” Nonaka suggested that companies use metaphors and organizational redundancy to focus thinking, encourage dialogue, and make tacit, instinctively understood ideas explicit.

A learning organization is an organization skilled at creating, acquiring, and transferring knowledge, and at modifying its behavior to reflect new knowledge and insights.

This definition begins with a simple truth: new ideas are essential if learning is to take place. Sometimes they are created de novo, through flashes of insight or creativity; at other times they arrive from outside the organization or are communicated by knowledgeable insiders. Whatever their source, these ideas are the trigger for organizational improvement. But they cannot by themselves create a learning organization. Without accompanying changes in the way that work gets done, only the potential for improvement exists.

This is a surprisingly stringent test for it rules out a number of obvious candidates for learning organizations. For instance, most universities and higher education Institutions fail to qualify.

In Schools that Learn, Peter Senge argues that teachers, administrators, and other members of school communities must learn how to build their own capacity; that is, they must develop the capacity to learn. From Senge’s perspective, real improvement will only occur if the people responsible for implementation design the change itself: “It is becoming clear that schools can be re-created, made vital, and sustainably renewed not by fiat or command, and not by regulation, but by taking the learning orientation” . Senge, author of the best-selling The Fifth Discipline, has written a highly readable companion book directly focused on education. Individuals familiar with his earlier work will immediately recognize the five skills or disciplines at the heart of the learning orientation he proposes: developing personal mastery, creating shared mental models, establishing a shared vision, engaging in team learning, and thinking systemically. Collectively, these five disciplines represent the component skills underlying the learning process. According to Senge, if an individual, group, or organization develops the capacity to do each of the disciplines well, they will have become proficient in learning itself.

While there can be no ‘algorithm’ for creating a culture of learning, the following is a good guide : Six steps to create a culture of learning: https://www.personneltoday.com/hr/6-steps-to-creating-a-culture-of-engaged-learning/

They are:

1: Show that learning is valued (and start at onboarding)

2: Create active engagement in learning

3: Build leadership buy-in

4: Deliver consumer-like experiences

5: Drive deeper engagement over time

6: Create a ‘consumer to contributor’ cycle

I end this post with sharing of a list of 10 TED talks about learning : https://www.growthengineering.co.uk/10-favourite-ted-talks-learning/. The talks are:

1: The Life-long Learner – Bernie Dunlap

2: Learning How To Learn – Rodrigo Arboleda

3: Learning Styles And The Importance Of Critical Self-Reflection – Dr. Tesia Marshik

4: The Nerd’s Guide To Learning Everything Online – John Green

5: Let’s Use Video To Help Reinvent Education  – Salman Khan

6: Let’s Teach For Mastery, Not Test Scores – Salman Khan

7: Bring On The Learning Revolution – Sir Ken Robinson

8: How Games Make Kids Smarter – Gabe Zichermann

9: Smash Fear, Learn Anything – Tim Ferriss

10: The Call To Learn – Clifford Stoll

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Learning 321:

Learning 321: a community where the members learn something new everyday:

Learning 321 is a learning community seeded by Prof.MM Pant to explore social and collaborative learning for the 3rd decade of the 21st Century. It is a unique group where members propose what they want to learn. And other members of the group work to create explanatory content of 10, 20 or 60 minutes,with links to curated additional resources. Building on the 2009 Nobel Prize (Economics) work of Elinor Ostrom, and forced by the Covid 19 pandemic it explores a community based model of learning.

This is the link to join the WhatsApp group:https://chat.whatsapp.com/BVsQkx6x

If this doesn’t work for any reason, you may send a WhatsApp message ( this is a WhatsApp group) to Prof. MM Pant at+919810073724.

The schedule for January 2021 is as follows: 

Everyday the post(s) are made at about 4pm. 

Friday 1st January: Arunjee: Humour in the classroom 

Saturday 2nd January: Manish Kumar: Quantum/Classical hybrid approach to AI/ML/1

Sunday January 3rd: Dr Chinu Agarwal: Mindful Livng

Monday January 4th: Dr Ramesh C Sharma: Fake News

Tuesday January 5th: Dolly Bhasin: Fashiontech/ Sustainable Fashion

Wednesday January 6th: Dr Jyoti Vora:Narrative of the nutraceutical… let food be thy medicine…..a Vedic dictum 

Thursday January 7th: Kapil Murdia: Creating awesome tech products/1

Friday January 8th: Bhavna Chibber: Positive aspects of social media 

Saturday January 9th: Dr Prakash Moghe : Beyond Abacus

Sunday January 10th: MM Pant: Developing a culture of learning

Monday January 11th: Dr Ramesh C Sharma: Virtual Worlds in education 

Tuesday January 12th: Sandeep Khanna: Cyber Safety

Wednesday January 13th: Dr Jyoti Vora: The wondrous realms of rock salt ( sendhaa namak)

Thursday January 14th: Kapil Murdia: Creating awesome tech products/2

Friday January 15th : Bhavna Chibber: Ambidextrous- blessing or skill

Saturday January 16th: Dr Prakash Moghe: The Mathematics of life

Sunday January 17th: MM Pant: Towards a Science of Learning

Monday January 18th: Dr Ramesh C Sharma : Gamification in education 

Tuesday January 19th: Archana Gaba: Cognitive Flexibility

Wednesday January 20th: Dr Jyoti Vora: Heavenly honey… a haven of wellness 

Thursday January 21st: Kapil Murdia: Self-improvement 101/1

Friday January 22nd : Bhavna Chibber: Principle behind vaccination

Saturday January 23rd: Dr Prakash Moghe:Constants and variables

Sunday January 24th: Manish Kumar: Quantum/Classical hybrid approach to AI/ML/2

Monday January 25th: Dr Ramesh C Sharma: Learning Analytics in education 

Tuesday January 26th: Vinod Dumblekar : Experiential, vicarious and fun learning

Wednesday January 27th: Dr Jyoti Vora: The jewel of good health…. jaswandaa (hibiscus)

Thursday January 28th: Kapil Murdia: Ideation to execution

Friday January 29th: Bhavna Chibber: How to become a better self-learner?

Saturday January 30th: Dr Prakash Moghe: The concept of Limits

Sunday January 31st: MM Pant: A personal Philosophy

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Paradigm shifts in education:

“ Paradigm shifts in education”

The phrase ‘paradigm shift’ gained traction from the well known book (1962)  by Thomas Kuhn “ The structure of Scientific Revolutions” which challenged the earlier view that Science progressed almost linearly as “development-by-accumulation” of accepted facts and theories. Kuhn argued for an episodic model in which periods of conceptual continuity, which Kuhn referred to as periods of ” normal science”, were interrupted by periods of revolutionary science. 

Kuhn’s view was  that a ‘paradigm shift’ was not a logically determinate procedure. He took the example of the Copernican revolution to make his point. The Darwinian theory of evolution of species and Alfred Wegener’s theory of tectonic plates are similar paradigm shifts as are Quantum principles. 

Clayton Christensen went beyond the focus on Scientific Research and referred to similar trends in innovation as ‘incremental’ or ‘disruptive’. 

In the field of education and learning we are witnessing two major revolutions, the 4th Industrial Revolution which was heralded by Prof Klaus Schwab at the World Economic Forum in January 2016 and the 4th education revolution, which is the title of a 2018 book by Sir Anthony Seldon. I strongly urge all educators to read this book, where two important assertions by him are that there have been only 3 education revolutions so far, and that education is the Cinderella of the AI story. Fundamental paradigm shifts in the education and learning domain are therefore inevitable. And like the proverbial last straw on the camel’s back, we had this unexpected Covid 19 pandemic. 

There are several manifestations of this paradigm shift, but in this post, I will confine myself to a few important elements, where I have created some approaches to align education to the future needs and expectations.

Referring to the need for new skills in the 4th Industrial Age, the WEF has listed 10 top skills for 2020 and beyond. For these 10 skills and  two more skills of Computational Thinking and First Principles, I have created a suite of 12 WhatsApp weekend courses whose details are available at: https://mmpant.com/ss321/

Students and teachers are the primary stakeholders in any educational system. The parents of young learners also have an important role, and they are learners too as lifelong learners. Everything else is overheads. 

The first aspect of paradigm shift is the transformation of the disinterested and passive learner ( who has to be compulsorily educated to acquire minimum levels of learning) to an active self- motivated and self-directed learner with an immense curiosity and a virtually insatiable appetite for learning that lasts a lifetime. For such a learner, learning has changed from a spectator sport to a participative one. Such a learner has many more opportunities for learning, going beyond the confines of the classroom. I have created a set of 4 weeklong WhatsApp courses to facilitate this transition. They are: 

WLL01: Learning with WhatsApp, other Mobile Apps and MOOCs

WLL02: Dispositions for success

WLL03: Learning Agility

WLL04: Getting a world class education in your hands

To know more about these, please visit : https://mmpant.com/asl/

This transition will not be an instantaneous binary on/off type, but somewhat graded and gradual over time during which the learner progresses over several levels. 

In 1997 David Nunan had proposed a scheme of five levels for encouraging learner autonomy in relationship to use of learning materials. He labelled these 5 levels as : awareness, involvement, intervention, creation and transcendence. 

In the year 2020, I have created my own framework for 5 levels of autonomous learners, and also listed the set of 10 skills expected at each level. I am sharing that set of skills towards the end of this post.

I have included in this list of skills, the abilities to deploy artificial Intelligence tools and Apps  to engage in self-directed learning. I am proposing the following levels of an autonomous learner. This structure is analogous to the levels of self-driving cars approved by the Society of Automobile Engineers ( SAE). 

Level 0: No self-learning disposition or ability

Level 1: Some inclination towards self-learning and core abilities of self-learning

Level 2: An autonomous learning system that supports the learner but can be superseded by the human educator ( an AI fluent SmartEducator) 

Level 3: The Autonomous learning enabling system requests to be superseded by the human educator ( an AI fluent SmartEducator)

Level 4: A fully autonomous learner that can function well in limited domains ( cognitive geo-fencing) 

Level 5: Fully autonomous learner with the right dispositions and skills

The second element is the educator.

The most important quality desirable in  a modern 21st Century educator is one of being a lifelong learner. Educators are lifelong learners that develop other lifelong learners. They appreciate  that transferring  the knowledge of subjects ( and preparing for terminal Board exams or entrance tests for further education) is far less important than fostering a desire to learn, the ability of learning to learn fast , and building of learning power. 

Sir Anthony Seldon called on educators everywhere to open their eyes to the fast approaching revolution in Artificial Intelligence, and asked if we are ready to embrace this revolution and shape AI to the best advantage of education and humanity as a whole. The impact of AI is acknowledged in the NEP2020. “23.8. This policy has been formulated at a time when an unquestionably disruptive technology, Artificial Intelligence (AI) has emerged. As the cost of AI-based prediction falls, AI will be able to match or outperform – and therefore be a valuable aid to – even skilled professionals such as doctors in certain predictive tasks. “

But adopting  the Ostrich policy, it ignores readying educators to use it in educational applications. However like the Giraffe, we wish to stick out our neck to see what’s coming, and prepare ourselves for that. Inspired by Newton who said that “ If I have seen further than others, it is by standing on the shoulders of giants”, we are building on the foundations already laid down by others. 

Anthony Seldon in his book referred above has said that Artificial Intelligence will significantly transform education, and that educators should take a lead in the deployment of AI in education. In analogy to the 5 levels of the learner, I have proposed 5 levels of the future ready educators. 

These are : 

Level 1: Progressive Educators

Level 2: SmartEducators

Level 3: AI-familiar SmartEducators

Level 4: AI-fluent SmartEducators

Level 5: AInEd Evangelist 

Descriptors have been created for each of these 5 levels, and programs have been developed to reach each of these levels. 

To be at Level 1, the educator seeks knowledge and information on Educational innovation including pedagogies, good practices and uses digital resources to keep up with the changing paradigms of education. 

To reach Level 2, of SmartEducator I have created a suite of 4 one weeklong programs that can be comfortably pursued using WhatsApp with mobile phones in  one month. For details visit : https://mmpant.com/smarteducators/

These are : 

WLL09: Becoming an Independent Educator

WLL10: WhatsApp for Educators

WLLL11: Artificial Intelligence based Teaching-Learning 

WLL12: Educational Leadership in the age of Artificial Intelligence 

At this stage, most educators, especially those who do not have an irrational fear of computers would want to learn about AI in some depth and try their hands working with AI applications in education, and even developing some new AI based educational solutions themselves. 

To be at Level 3, there is a one month program with the following 4 weeklong courses: 

AIFSE01: The Landscape of AI in education 

AIFSE02: From Data to Learning algorithms

AIFSE03: Learning to learn AI/ML

AIFSE04: Deep Learning for educators 

Further information about these courses can be seen at : https://mmpant.com/aifse/

At the end of the term, having done these 4 courses they are ready to become “AI-familiar SmartEducator”. 

Those who are now motivated and excited to learn more and at depth, will pursue the following two weekend courses:

AIFSE05: Emerging Technologies in education

AIFSE06: Hands on AI experience

Having done all the 6 courses as well as a project of AI application in education, they will qualify for “ AI-fluent SmartEducator”. 

The 5th level of AInEd Evangelist is achieved by being a Practioner in the field for a few years, and having written books, delivered training programs, workshops etc. to help spread AI awareness amongst other educators. Both educators who have become adept in AI technologies and AI professionals who are interested in education and have applied themselves towards learning interventions will comprise membership of this category. 

Having described in detail, the levels of the educator, it is now time to share the full list of dispositions and skills at each level of learner autonomy. Here is a framework that is ” Made in India, but made for the world”. 

Level 5: (10 skills) Fully autonomous learner with the right dispositions and skills

  • Developing foresight
  • Identify Self-learning needs 
  • Set self learning goals
  • Effective Decision making
  • Research competence 
  • Ability to teach others 
  • Developing dispositions for success
  • Avoiding stupidity
  • Ideas: their creation and dissemination 
  • Computational Thinking

Level 4: (10 skills) A fully autonomous learner that can function well in limited domains ( cognitive geo-fencing) 

  • Identify your own learning needs
  • Set learning goals to address those needs
  • Identify experts and mavens in your domain to follow and get inspiration from
  • Curate appropriate resources to achieve your learning goal. 
  • Process the knowledge resources to achieve your learning goal
  • Apply appropriate learning strategies
  • Emotional Intelligence
  • Empathy
  • Finding your Ikigai
  • Evaluate the outcomes of your learning

Level 3: (10 skills) The Autonomous learning enabling system requests to be superseded by the human educator ( an AI fluent SmartEducator)

  • Cognitive Flexibility
  • Negotiation Skills
  • Complex problem solving
  • First principles
  • Social Learning/ Peer Learning 
  • Multitasking
  • Information Management
  • Team orientation 
  • Attention to detail
  • Digital presence and communication 

Level 2: ( 10 skills) An autonomous learning system that supports the learner but can be superseded by the human educator ( an AI fluent SmartEducator) 

  • Using mind maps
  • Learning agility: learning, unlearning and re-learning
  • Ultralearning
  • Google search skills
  • Using AI apps for better learning: text to speech, speech to text, machine translation
  • Learning from YouTube
  • Using an app to get the text of an audio or video narration
  • Grit : determination to overcome one’s barriers to learning 
  • Open mindedness
  • Knowing your element

Level 1: (10 skills) Some inclination towards self-learning and core abilities of self-learning 

  • Creative thinking
  • Critical thinking
  • Fact checking
  • Effective learning techniques
  • Time Management 
  • Avoiding/ overcoming procrastination 
  • Learning from WhatsApp 
  • Learning from other mobile Apps
  • Learning from MOOCs
  • Enhancing the span of attention (focus) 

Level 0: No self-learning disposition or ability.

The 50 listed above  can be acquired by joining a self-learning for future readiness  program given at : https://mmpant.com/mfr/

Finally the third important element of this paradigm shift is the role of parents. 

This is acknowledged in the initial part of the NEP2020 document. 

“1.1. Over 85% of a child’s cumulative brain development occurs prior to the age of 6, indicating the critical importance of appropriate care and stimulation of the brain in a child’s early years for healthy brain development and growth.”

That is why I have often said that the home is the first school, the mother’s lap the first classroom and the mother is the most important and effective teacher. 

An educated and informed mother is therefore the best assurance of a child’s holistic development in the foundational years. As universal secondary education  is achieved, almost all parents ( with appropriate  online remote training modules) will be able to fulfil the learning needs of their own children at the school stage. And when the GER of 50% is achieved, all parents will be able to build self-learning capabilities and life-long learning dispositions in their children. A desire to learn, and the mindset and capabilities to learn very well. 

The paradigm of the Kothari education commission (1964) that ‘ the destiny of India is now being shaped in her classrooms’ would have changed to the new paradigm of ‘ the destiny of India is in the hands of its children, their teachers and parents’.

In the coming years, learning will not be only at the formal School, College and University, but continue throughout life. Karl Jung had pointed to the need for the ‘School for the 2nd half of life’. We have created a set of 4 WhatsApp delivered weeklong courses towards this goal. Details are given at: https://mmpant.com/msai/

To get such lifelong learners to start their learning journeys, we have created a suite of Weekend Evening one hour talks that facilitate good learning techniques and thinking skills that are detailed at: https://mmpant.com/weet/

For those who have a sudden urge to learn, we have a set of 3 one hour courses that can be joined anytime. More information at: https://mmpant.com/atl/

Quantum Technologies including Quantum Computing are progressing very fast, and this is the right time to prepare a Quantum Ready workforce. We have accordingly developed a suite of 5 courses towards this. They are QR1: Quantum Readiness, QR2: Quantum Concepts, QR3: Quantum Computing, QR4: Quantum Botany and QR5: Quantum Technologies. More details at : https://mmpant.com/qr/

When the British started ruling India, they enquired as to how education was organised in the country, and were told that it is organised by the community. And they took a decision that such community based learning must be replaced by state delivered education and the community delivered education must be declared illegal. Mahatma Gandhi had criticised this, but to date we are continuing with the British model, with a few occasional cheers for Finland.

Elinor Ostrom was the first woman to win the 2009 Nobel Prize in economics for her work demonstrating the effectiveness of the community than either the Government or the private corporate sector. In pursuance of her work, and forced by the Covid 19 pandemic, I have seeded a learning community to explore social and collaborative learning for the 3rd decade of the 21st Century.  It is a unique group where members propose what they want to learAnd other members of the group work to create explanatory content of 10, 20 or 60 minutes,with links to curated additional resources. It functions primarily as a WhatsApp group.  To join the group, please send a WhatsApp message to Prof MM Pant at +919810073724

In the realm of Physics, the earlier paradigm is called ‘ Classical Physics’ and the first decades of the 20th Century saw the development of Quantum Mechanics. In the field of learning, we may refer to education models till the 20th Century as ‘traditional education’. Since the new Science of Learning is to develop now building upon the advances in Machine Learning and Neurosciences, I refer to the innovations that we will see during the coming decade as ‘Learning 321’ for Education in the 3rd decade of the  21st Century. 

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Swarm Intelligence:

Swarm Intelligence:

Swarm intelligence  deals with natural and artificial systems composed of many individuals that coordinate using self-organisation. Examples of such systems  are colonies of ants and termites, schools of fish, flocks of birds, herds of land animals.

Swarm intelligence is a multidisciplinary field since systems with the above characteristics can be observed in a variety of domains. Research in swarm intelligence is often classified according to the following criteria.

Natural vs. Artificial: It is customary to divide swarm intelligence research into two areas according to the nature of the systems under analysis. We speak therefore of natural swarm intelligence research, where biological systems are studied; and of artificial swarm intelligence, where human artifacts are studied.

Scientific vs. Engineering: The goal of the scientific stream is to model swarm intelligence systems  and understand the mechanisms that allow a system as a whole to behave in a coordinated way as a result of local individual-individual and individual-environment interactions. On the other hand, the goal of the engineering stream is to exploit the understanding developed by the scientific stream in order to design systems that solve problems of practical relevance.

Natural/Scientific: Foraging Behavior of Ants

In a now classic experiment done in 1990, Deneubourg and his group showed that, when given the choice between two paths of different length joining the nest to a food source, a colony of ants has a high probability to collectively choose the shorter one. Deneubourg has shown that this behavior can be explained via a simple probabilistic model in which each ant decides where to go by taking random decisions based on the intensity of pheromone perceived on the ground, the pheromone being deposited by the ants while moving from the nest to the food source and back.

Artificial/Scientific: Clustering by a Swarm of Robots

Several ant species cluster corpses to form cemeteries. Deneubourg et al. (1991) were among the first to propose a distributed probabilistic model to explain this clustering behavior. In their model, ants pick up and drop items with probabilities that depend on information on corpse density which is locally available to the ants. Beckers et al. (1994) have programmed a group of robots to implement a similar clustering behavior demonstrating in this way one of the first swarm intelligence scientific oriented studies in which artificial agents were used.

Natural/Engineering: Exploitation of collective behaviors of animal societies

A possible development of swarm intelligence is the controlled exploitation of the collective behavior of animal societies. For example, small insect-like robots are used as lures to influence the behavior of a group of cockroaches. The technology developed within this project could be applied to various domains including agriculture and cattle breeding.

Properties of a Swarm Intelligence System

The typical swarm intelligence system has the following properties:

  • it is composed of many individuals;
  • the individuals are relatively homogeneous (i.e., they are either all identical or they belong to a few typologies);
  • the interactions among the individuals are based on simple behavioral rules that exploit only local information that the individuals exchange directly or via the environment (stigmergy);
  • the overall behaviour of the system results from the interactions of individuals with each other and with their environment, that is, the group behavior self-organizes.

The characterizing property of a swarm intelligence system is its ability to act in a coordinated way without the presence of a coordinator or of an external controller. Many examples can be observed in nature of swarms that perform some collective behavior without any individual controlling the group, or being aware of the overall group behavior. Notwithstanding the lack of individuals in charge of the group, the swarm as a whole can show an intelligent behavior. This is the result of the interaction of spatially neighboring individuals that act on the basis of simple rules.

Most often, the behavior of each individual of the swarm is described in probabilistic terms: Each individual has a stochastic behavior that depends on his local perception of the neighborhood.

Because of the above properties, it is possible to design swarm intelligence system that are scalable, parallel, and fault tolerant.

Studies and Applications of Swarm Intelligence

Clustering Behavior of Ants

Ants build cemeteries by collecting dead bodies into a single place in the nest. They also organize the spatial disposition of larvae into clusters with the younger, smaller larvae in the cluster center and the older ones at its periphery. This clustering behavior has motivated a number of scientific studies. Scientists have built simple probabilistic models of these behaviors and have tested them in simulation (Bonabeau et al. 1999). The basic models state that an unloaded ant has a probability to pick up a corpse or a larva that is inversely proportional to their locally perceived density, while the probability that a loaded ant has to drop the carried item is proportional to the local density of similar items. This model has been validated against experimental data obtained with real ants. In the taxonomy this is an example of natural/scientific swarm intelligence system.

Nest Building Behavior of Wasps and Termites

Wasps build nests with a highly complex internal structure that is well beyond the cognitive capabilities of a single wasp. Termites build nests whose dimensions (they can reach many meters of diameter and height) are enormous when compared to a single individual, which can measure as little as a few millimeters. Scientists have been studying the coordination mechanisms that allow the construction of these structures. In the taxonomy this is an example of natural/scientific swarm intelligence system.

Flocking and Schooling in Birds and Fish

Flocking and schooling are examples of highly coordinated group behaviors exhibited by large groups of birds and fish. Scientists have shown that these elegant swarm-level behaviors can be understood as the result of a self-organized process where no leader is in charge and each individual bases its movement decisions solely on locally available information: the distance, perceived speed, and direction of movement of neighbours. In the taxonomy these are examples respectively of natural/scientific and artificial/engineering swarm intelligence systems.

Ant colony optimisation: 

Ant colony optimization  is a population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. It is inspired by the above-described foraging behavior of ant colonies. In ant colony optimization (ACO), a set of software agents called “artificial ants” search for good solutions to a given optimization problem transformed into the problem of finding the minimum cost path on a weighted graph. The artificial ants incrementally build solutions by moving on the graph. The solution construction process is stochastic and is biased by a pheromone model, that is, a set of parameters associated with graph components (either nodes or edges) the values of which are modified at runtime by the ants. ACO has been applied successfully to many classical combinatorial optimization problems, as well as to discrete optimization problems that have stochastic and/or dynamic components. Ant colony optimization is probably the most successful example of artificial/engineering swarm intelligence system with numerous applications to real-world problems.

Particle swarm optimisation: 

Particle swarm optimization is a population based stochastic optimization technique for the solution of continuous optimization problems. It is inspired by social behaviors in flocks of birds and schools of fish. In particle swarm optimization (PSO), a set of software agents called particles search for good solutions to a given continuous optimization problem. Each particle is a solution of the considered problem and uses its own experience and the experience of neighbor particles to choose how to move in the search space. In practice, in the initialization phase each particle is given a random initial position and an initial velocity. The position of the particle represents a solution of the problem and has therefore a value, given by the objective function. While moving in the search space, particles memorize the position of the best solution they found. At each iteration of the algorithm, each particle moves with a velocity that is a weighted sum of three components: the old velocity, a velocity component that drives the particle towards the location in the search space where it previously found the best solution so far, and a velocity component that drives the particle towards the location in the search space where the neighbor particles found the best solution so far. PSO has been applied to many different problems and is another example of successful artificial/engineering swarm intelligence system.

Swarm-based Network Management

The first swarm-based approaches to network management were proposed in 1996 by Schoonderwoerd et al., and in 1998 by Di Caro and Dorigo. Schoonderwoerd et al. proposed Ant-based Control (ABC), an algorithm for routing and load balancing in circuit-switched networks; Di Caro and Dorigo proposed AntNet, an algorithm for routing in packet-switched networks. While ABC was a proof-of-concept, AntNet, which is an ACO algorithm, was compared to many state-of-the-art algorithms and its performance was found to be competitive especially in situation of highly dynamic and stochastic data traffic as can be observed in Internet-like networks. An extension of AntNet has been successfully applied to ad-hoc networks (Di Caro, Ducatelle and Gambardella 2005). These algorithms are another example of successful artificial/engineering swarm intelligence system.

Cooperative Behavior in Swarms of Robots

There are a number of swarm behaviors observed in natural systems that have inspired innovative ways of solving problems by using swarms of robots. This is what is called swarm robotics. In other words, swarm robotics is the application of swarm intelligence principles to the control of swarms of robots. As with swarm intelligence systems in general, swarm robotics systems can have either a scientific or an engineering flavour. Clustering in a swarm of robots was mentioned above as an example of artificial/scientific system. An example of artificial/engineering swarm intelligence system is the collective transport of an item too heavy for a single robot, a behavior also often observed in ant colonies.

The text in this blog comprises excerpts from the following Sources : 

1: http://www.scholarpedia.org/article/Swarm_intelligence

2: https://en.wikipedia.org/wiki/Swarm_intelligence

3: https://www.sciencedirect.com/topics/engineering/swarm-intelligence

And here are a few videos that will help in appreciating the concept and usefulness of Swarm intelligence : 

1: Why Swarm Intelligence (~ 3minutes) : https://youtu.be/jfoAYg-gk98/

2: Inside the ant colony : (~5 minutes) : https://youtu.be/vG-QZOTc5_Q

3: Five principles of Swarm Intelligence(~2 minutes): https://youtu.be/axxXz2BM0yw

4: Taming the swarm; collective artificial intelligence( ~20 minutes): https://youtu.be/LHgVR0lzFJc

5: Changing the world through Swarm Intelligence (~20 minutes) : https://youtu.be/zdiFV-AFbBA

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Future readiness:

Skills and competencies for future-readiness: 

One of the harsh truths about the 3rd decade of the 21st Century which is upon us, is the pace of change that results in “the skills acquired over a lifetime becoming obsolete in an instant”. And the only way to “immunise” oneself against it is to follow the advice of Alvin Toffler and continuously “ learn, unlearn and re-learn”. 

In many games a good player keeps track of where the ball is going to, and prepares to be ready to perform his action at the right time and place. In board games like chess or Go, one has to anticipate not only the next move, but many future moves also. In order to be future ready, one has to anticipate the most valued skills of the future that will help one thrive, prosper and flourish. 

Edward de Bono lamented the state of future readiness of education with the following words : Education is like a ship where the lights have gone out, the rudder is broken, the crew is demoralised and it’s drifting in the wrong direction. You can fly in a new captain, mend the lights, fix the rudder and inspire the crew but you’ll still be heading in the wrong direction.

One way to know of skills for the future is to follow reports of organisations like the WEF, the World Bank, the ILO, OECD and other similar organisations. For example a recent report from the WEF “ What are the top 10 emerging  technologies of the year? “ lists these:

  • micro-needles for painless injections and tests 
  • sun-powered Chemistry
  • Virtual patients
  • Spatial computing
  • Digital Medicine
  • Electric aviation
  • Lower-carbon cement
  • Quantum Sensing
  • Green Hydrogen
  • Whole-genome synthesis

The WEF had in the year 2020 produced a list of the top 10 skills in demand and compared it with the top 10 skills in 2015. The top 10 skills of 2020 were :Complex Problem Solving , Critical Thinking, Creativity, People Management, Co-ordinating with others, Emotional Intelligence, judgement and decision making, Service Orientation, Negotiation and Cognitive Flexibility. 

It is important to note that Complex Problem Solving was at number 1 in 2015 and continues to be so in 2020. Creativity has moved from no.10 in 2015 to no.3 in 2020, and Cognitive Flexibility was not even included in the top 10 skills for 2015. 

Cognitive flexibility has been described as the mental ability to switch between thinking about two different concepts, and to think about multiple concepts simultaneously. Cognitive flexibility is usually described as one of the executive functions. Two subcategories of cognitive flexibility are task switching and cognitive shifting, depending on whether the change happens unconsciously or consciously, respectively.

Computational Thinking was brought to the attention of the  education community in 2006 as a result of an article on the subject by Jeanette Wing. It 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 subjects at school. The 4 steps of Computational Thinking are: Abstraction, Decomposition, Algorithms and Evaluation. 

First Principles is an approach adopted and evangelised by Elon Musk. The term was coined more than 2,000 years ago by the ancient Greek philosopher Aristotle, who believed we learn more by understanding a subject’s fundamental principles.   First principles thinking is the act of boiling a process down to the fundamental parts that you know are true and building up from there. 

Applying first principles to anticipating the future requires a knowledge of where the frontiers of human knowledge are moving, the landscape of patenting and finally where investments are being made by the Governments and venture capitalists. It is at the intersection of these three that new opportunities will emerge for the young.

In addition to the outputs of various leading research Institutes and organisations, a simple way to observe where the frontiers of knowledge are moving, is to follow the Nobel Prizes awarded every year in the fields of Physics, Chemistry, Physiology or Medicine, Literature and Peace. The Nobel Memorial Prize in Economic Sciences, officially the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel is also included in the list of Nobel Prizes awarded every year. The website is nobelprize.org 

There is no Nobel Prize for Mathematics, but The Fields Medal is awarded to recognize outstanding mathematical achievement by Mathematicians aged 40 years or under for existing work and for the promise of future achievement. It is not awarded every year but once in every 4 years. 

The A. M. Turing Award is an annual prize given by the Association for Computing Machinery (ACM) for contributions “of lasting and major technical importance to the computer field”. It is generally recognized as the highest distinction in computer science or the “ Nobel Prize of Computing”. 

Self-learning is the most important skill for becoming future proof. And in this journey of self-learning one develops competence, comprehension and cognitive flexibility. This is not a sequential path. One may develop competence in something without fully comprehending how it works. Or one may comprehend the principles but take time in honing the skills. Being fluent in both gives cognitive flexibility. 

Professor Eric Hanushek of Stanford University in collaboration with Ludger Woessmann emphasised the role of education in promoting economic growth, with a particular focus on the role of knowledge capital, or the aggregate skills of a country. It concludes that there is strong evidence that the cognitive skills of the population – rather than mere school attainment – are powerfully related to long-run economic growth. The relationship between knowledge capital and growth proves extremely robust in empirical applications. The effect of skills is complementary to the quality of economic institutions. Growth simulations reveal that the long-run rewards to educational quality are large but also require patience.

An appropriate example in this context would be the efforts to raise a Quantum ready workforce. 

We may conclude that the effort required in the core and allied high value skills will greatly enhance the knowledge capital of the individual, a community and the country. 

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Learning Communities:

Learning Communities:

It is interesting to note that historically, education in India was managed by the community and it was a job rather well done. We were the most prosperous country till about 250 years ago, and it is worth taking a look at Macaulay’s statement on February 2, 1835 in the British Parliament:

“I have travelled the length and breadth of India and I did not meet a single person who was a thief. I have seen such affluence in that country, such competent individuals and such talent that I do not think we will be able to conquer that land so long as we do not break its cultural and ethical backbone. I therefore state that we change the ancient education system and culture of India because if the inhabitants of India begin to think that the ideas and thoughts of foreigners, of Englishmen, are better than and

 superior to their own, then they will lose their culture and self-respect and they will become a dependent nation, which is what we need.”

Mahatma Gandhi referred to this in his Chatham House speech in London on October 20th 1931, before a select audience said “I say without figures of mine being successfully challenged that India today is more illiterate than it was 50 or 100 years before, and so is Burma, because the British administrators when they came to India, instead of looking at things as they were, began to root them out. They scratched the soil and began to look at the root and left the root as it is and let the beautiful tree perish”.

While this has been widely circulated, there are some who contest the veracity of the statement attributed to McCauley. 

At present, education is almost entirely driven by the Government. Education in India is primarily a State matter although there are several elements in the concurrent list. The Right to Education Act takes this further in prescribing free and compulsory Government provided schooling from ages 6 to 14. There are enough indicators that by and large this education system is a failed system, even with the changes sought to be made by the NEP2020. 

We are in complex rapidly changing times, and the command and control system is no longer an effective and efficient one. 

Sometime before 1989, a Soviet official asked economist Paul Seabright who was in charge of London’s bread supply. Seabright gave him an answer that is comical but also true: ‘nobody’. The bread we eat turns up on our tables thanks to an incredible team effort (bakers, machinists, electricity suppliers, distributors etc etc). And even more incredibly, there is no-one in charge of that team.

It just happens.

The future of education will also be with learning communities, and not multiple bureaucracies. In the future  we are likely to be in  a situation when harnessing the cognitive surplus of the community would deal with the educational challenges of the country much more effectively than the low quality State apparatus. Prof. Elinor Ostrom was awarded the 2009 Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, which she shared with Oliver E. Williamson, for “her analysis of economic governance, especially the commons”. She was the first woman to win the prize in this category. Her work was associated with the new institutional economics and the resurgence of political economy. She passed away on June 12th, 2012 but will long be remembered for convincing demonstrations that people can work together to do what neither the Government nor the private Corporates can. The ‘tragedy of the commons‘ enunciated by Garret Harding is giving way to the wisdom of the crowds in the post Internet World.

Maybe in another decade or so, the Nobel Prize winning ideas of Prof. Elinor Ostrom, with teachers and learners deploying the power of Artificial Intelligence and Machine Learning will create  a community led education that serves the good of the community. May be Swami Vivekanand’s exhortation of ‘Arise, awake and stop not till the goal is reached’ is what we have to follow.

There is a famous quote  from Simon Sinek : A community is a group of people who agree to grow together. 

According to the Wikipedia a learning community is a group of people who share common academic goals and attitudes and meet semi-regularly to collaborate on classwork. Such communities have become the template for a cohort-based approach to education . This may be based on an advanced kind of educational or ‘pedagogical’ design.

Elements that contribute to a culture of learning and innovation.

  1. Shared Vision– Together- leaders, teachers, families and the community are pushing boundaries and supporting each other to design learning experiences that meet the needs of their unique population.
  2. Co-creation– Through collaboration, reflection and multiple iterations, there are lessons learned that inform next steps in powerful ways.
  3. Risk-taking– They support one another but will also challenge to take their ideas further and continue to innovate to improve student outcomes.
  4. Learning Environment–  The learning environment critical and must be attended to as the environment you’re in shapes your behavior.
  5. Connect and Share- Innovation requires time and commitment and it is important to share the success along the way.
  6. Reciprocal Accountability–  All stakeholders collectively determine what to stop doing and start doing to move towards the vision in order to create powerful learning for all.
  7. Build on Strengths–For example  one student may take  a coding class and another student was able to be part of the school’s new crew and work on broadcast journalism.

Unlike managers who look at numbers and targets, a learning community fosters a culture of learning, and the rest follows. We lost out on a year because the culture of learning had not been developed. 

Some elements of creating a culture of learning are: 

1: Show them you’re a learner too.

2: Encourage creativity

3: Make it meaningful

4: Flatten classroom walls

5: Demonstrate your passion

6: Respect your students

7: Provide variation

8: Implement enquiry as a stance

9: Play games

10: Encourage students to be responsible for their own learning

Some examples of the success of learning communities are the Scientific community, and the Silicon Valley or GitHub…..

Even Institutions like the Institute of Advanced Study Princeton and Bell Laboratories are really self-organising learning Communities. 

Links for further information: 

Wikipedia article on online learning communities:https://en.wikipedia.org/wiki/Online_learning_community

Should we apply Ostrom’s design principles to online learning communities : https://www.mixedrealities.com/2011/10/07/should-we-apply-ostroms-design-principles-to-online-learning-communities/

How to maintain communities? : How to maintain communities?. A collective thought by …medium.com › archipelago-learning-collective › how-t…

About family learning communities: https://www.mbaea.org/media/documents/Young_Children__September_2014_Fami_AD789049637DF.pdf

10 ways to foster a love for learning : https://whatedsaid.wordpress.com/2010/07/09/10-ways-to-foster-a-love-of-learning/

Ways to foster love of learning : https://biglifejournal.com/blogs/blog/instill-love-learning-children

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Knowledge Representation

Knowledge Representation 

The frontiers of knowledge are always on the move across many dimensions and perhaps at varying speeds along each of these dimensions. Inter-disciplinary and cross-disciplinary knowledge is becoming more valuable. The question of how to organise and represent human knowledge is an old problem. But now it has assumed critical importance. Our ability to deal with the complex challenges of the future will depend upon our ability to manage knowledge organisation and representation. 

Knowledge representation refers to the technical problem of encoding human knowledge and reasoning  into a symbolic language that enables it to be processed by information systems. In systems biology, knowledge representation is used to infuse data with scientific concepts and understanding in order to maximize its utility for furthering scientific insight.

Here is the link to a good survey article on Knowledge Organisation: https://www.isko.org/cyclo/knowledge_organization

In this piece I draw attention to a few examples. In the future we may have automated Artificial Intelligence  tools for us to do so. 

  • The periodic table
  • Mind maps
  • Knowledge graphs 
  • Data representation 
  • Valency of ideas/concepts
  • Taxonomy/ Classification

The periodic table  (also known as the periodic table of elements) is organized so that scientists can quickly discern the properties of individual elements such as their mass, electron number, electron configuration and their unique chemical properties. Metals reside on the left side of the table, while non-metals reside on the right. Organizing the elements to help further our understanding was first provided by Dmitri Mendeleev.

The periodic table of the elements has their names, atomic number, symbol and mass is color-coded for easier reference by students and researchers.

The periodic table is the most important chemistry reference there is. It arranges all the known elements in an informative array. Elements are arranged left to right and top to bottom in order of increasing atomic number.  Order generally coincides with increasing atomic mass. 

The different rows of elements are called periods. The period number of an element signifies the highest energy level an electron in that element occupies (in the unexcited state). The number of electrons in a period increases as one traverses down the periodic table; therefore, as the energy level of the atom increases, the number of energy sub-levels per energy level increases.

Using the data in the table scientists, students, and others that are familiar with the periodic table can extract information concerning individual elements. For instance, a scientist can use carbon’s atomic mass  to determine how many carbon atoms there are in a 1 kilogram block of carbon.

People also gain information from the periodic table by looking at how it is put together. By examining an element’s position on the periodic table, one can infer the electron configuration. Elements that lie in the same column on the periodic table (called a “group”) have identical valence electron configurations and consequently behave in a similar fashion chemically. For instance, all the group 18 elements are inert gases. The periodic table contains an enormous amount of important information. People familiar with how the table is put together can quickly determine a significant amount of information about an element.

“The disappearing spoon” is a 2010 book about the periodic table written by science reporter Sam Kean. Sam Kean begins this book by explaining the basics of the periodic table and how it works. He explains the set-up of the table and why it is organized the way it is. He emphasizes the importance of its organization and justifies why it must be this way. He discusses how the periodic table would not function if it were not for the layout. He states that an element’s position describes its function and strength. He describes the table of elements as a castle and the elements as bricks to build this castle. He then discusses how the periodic table contains, and is organized into, metals, gases, noble gases, halogens, etc. Here is a link for further information on the periodic table: https://www.sigmaaldrich.com/technical-documents/articles/biology/periodic-table-of-elements-names.html

Mind maps: 

The Mind Map first conceptualised by Tony Buzan  is an easy way to brainstorm thoughts organically without worrying about order and structure. It allows you to visually structure your ideas to help with analysis and recall.

A Mind Map is a diagram for representing tasks, words, concepts, or items linked to and arranged around a central concept or subject using a non-linear graphical layout that allows the user to build an intuitive framework around a central concept. A Mind Map can turn a long list of monotonous information into a colorful, memorable and highly organized diagram that works in line with your brain’s natural way of doing things.

Mind Mapping is perfect for:

  • Brainstorming and visualizing concepts
  • Presenting and communicating ideas
  • Graphic organizers and electronic note books
  • Running meetings more effectively
  • Outlining reports and documents
  • Simplifying task and project management
  • Writing essays

Software for creating mind-maps: https://thedigitalprojectmanager.com/mind-mapping-software/

Knowledge Graphs:

A Knowledge Graph is a model of a knowledge domain created by subject-matter experts with the help of intelligent machine learning algorithms. It provides a structure and common interface for all of your data and enables the creation of smart multilateral relations throughout your databases. Structured as an additional virtual data layer, the Knowledge Graph lies on top of your existing databases or data sets to link all your data together at scale – be it structured or unstructured.

Knowledge graphs are data. They have to be stored, managed, extended, quality-assured and can be queried.

Knowledge graphs: https://www.poolparty.biz/what-is-a-knowledge-graph/

Data representation: 

Even before we start on Knowledge representation, we have the challenge of data representation. Let’s take a simple problem. Assume that the average distance of the earth from the sun is 93 million miles. Also let’s assume that the speed of light is 186,000 miles per second. The question: how much time does light take to reach from the surface of the sun to the surface of the earth. You may remember it from your school days as about 8 minutes, or you could calculate it by dividing 93000000/186000 to get 500 seconds, which is 8 minutes 20 seconds. Now try to do the same calculation using Roman arithmetic in which the numbers 1 to 10 are written as: I,II,III,IV,V,VI,VII,VIII,IX and X. And then we have L, C, D and M……for 50,100,500 and 1000. Feel free to  search the Internet to find the rules for Roman arithmetic. You will find that not only is it very difficult to write these two numbers, it is impossible to do the division in any reasonable time. 

  • Valency of ideas/concepts

We considered the organisation of the periodic table as a very important reference diagram in Chemistry. Another important concept in Chemistry is valency. The combining capacity of an atom is known as its valency. The number of bonds that an atom can form as part of a compound is expressed by the valency of the element. 

The concept has been extended to the field of linguistics. 

In linguistics, valency or valence is the number and type of arguments controlled by a predicate, content verbs being typical predicates. A major authority on the valency of the English verbs is Allerton (1982), who made the important distinction between semantic and syntactic valency. As we saw in the making of mind-maps, the number of connections of any Ned on the mind map may be considered as its valency. The value of an idea may become higher if it has a higher valency and connected with others. 

  • Taxonomy/ classification :

Taxonomy is the Science and practice of classification of things or concepts, including the principles underlying such classification. While it was originally used for biological classification, the word taxonomy is now used as a synonym for classification. For example, in the field of education Bloom’s Taxonomy is a standardized categorization of learning objectives in an educational context. In the present times, there would be considerable interest in Virus classification, taxonomic system for viruses. In the field of Computing the following are quite important: Folksonomy, classification based on user’s tags and search engine taxonomy, considered as a tool to improve relevance of search within a vertical domain. 

Abraham Maslow first introduced his concept of a hierarchy of needs in his 1943 paper “A Theory of Human Motivation” and his subsequent book Motivation and Personality. This hierarchy suggests that people are motivated to fulfill basic needs before moving on to other, more advanced needs. There are five different levels of Maslow’s hierarchy of needs. Maslow’s hierarchy of needs is also a taxonomy. 

In analogy, the different levels of self-driving cars is also a taxonomy. 

Level 5 (Full Driving Automation)

Level 4 (High Driving Automation)

Level 3 (Conditional Driving Automation)

Level 2 (Partial Driving Automation)

Level 1 (Driver Assistance)

Level 0 (No Driving Automation)

To see the descriptions for each of these levels : https://www.synopsys.com/automotive/autonomous-driving-levels.html

We now have machine learning to do the classification for you. An enhanced version of the universally accepted Dewey decimal classification for all library books and resources, would be evolved for all types of digital content and automated systems for classification, storage and retrieval. This is very important as the amount of data/knowledge is growing exponentially, with user generated content being a large fraction. The goal of such systems is not only classification and storage but more importantly efficient retrieval when needed. 

While the technologies of the day are changing very rapidly, we have more and more knowledge and data which is long-lived and this must be borne in mind. Long-lived digital data collections are increasingly crucial to research and education in science and engineering. A number of well-known factors have contributed to this phenomenon. Powerful and increasingly affordable sensors, processors, and automated equipment (for example, digital remote sensing, gene sequencers, micro arrays, and automated physical behavior simulations) have produced a proliferation of data in digital form. Reductions in storage costs have made it cost-effective to create and maintain large databases. And the existence of the Internet and other computer-based communications have made it easier to share data. As a result, researchers in such fields as genomics, climate modeling, and demographic studies increasingly conduct research using data originally generated by others and frequently access this data in large public databases found on the Internet.

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On being the right size:

On being the right size? 

There has been a lot of discussion on the NEP 2020 recommendations for higher education, and one of the new ideas is to have large Universities. This is stated at “10.1. The main thrust of this policy in higher education is to end the fragmentation of higher education by transforming higher education institutions into large multidisciplinary universities, colleges, and HEI clusters, each of which will aim to have 3,000 or more students. “

It reminded me of an essay that I had read during my School years, “ On being the right size” by JBS Haldane in a book of essays “Possible Worlds and Other Essays” by J.B.S. Haldane published in1927.  Here is a link to the essay for immediate reference. :http://www.phys.ufl.edu/courses/phy3221/spring10/HaldaneRightSize.pdf

Haldane explains the factors that affect the size of an animal and argues that there is an optimum size for every animal. The opening lines of the essay are:

The most obvious differences between different animals are differences of size, but for some reason the zoologists have paid singularly little attention to them. In a large textbook of zoology before me I find no indication that the eagle is larger than the sparrow, or the hippopotamus bigger than the hare, though some grudging admissions are made in the case of the mouse and the whale. But yet it is easy to show that a hare could not be as large as a hippopotamus or a whale as small as a herring. For every type of animal there is a most convenient size, and a large change in size inevitably carries with it a change of form.

Later it says that “And just as there is a best size for every animal, so the same is true for every human institution.”

Just as there is diversity in the animal kingdom, there would be diversity in the nature of educational institutions, and therefore different types of educational Institutions would have different optimum numbers. 

It seems that Dronacharya’s Gurukul had a very high level of excellence with very few students. 

We have pretty much diluted the higher education system with the goal of enhancing the GER to 50%, which again is reminiscent of the statement that “ growth for the sake of growth is the ideology of the cancer cell”. 

A co-ordinated balanced harmonious growth maintaining homeostasis is the principle of sustainable life. 

Similar considerations apply to matters of Governance and administration. In that essay Haldane writes “ I do not suppose that Henry Ford would find much difficulty in running Andorra or Luxembourg on a socialistic basis. He has already more men on his pay-roll than their population. It is conceivable that a syndicate of Fords, if we could find them, would make Belgium Ltd or Denmark Inc. pay their way.” 

In India we have seen that the number of states  have been regularly increasing. ( https://en.wikipedia.org/wiki/States_and_union_territories_of_India ) and India today comprises of 28 states and 7 Union territories. 

While most of India’s states were carved out of bigger ones over the years on linguistic lines, some have argued that ease of governance, rather than language, should be the key to the size of state. Those in favour of small states point to the economic growth of Jharkhand, Chhattisgarh and Uttarakhand that were created in 2000. And that of Haryana and Himachal earlier. Small states are easier to govern and people are closer to the decision makers. Smaller states also reduce diversity making policymaking more focused and management easier.

Why not: Some say that small states won’t be economically viable (only states rich in natural resources benefit) and some smaller states have been politically unstable. Bigger states, on the other hand, are about cohesion and stability. Having more states makes the central government’s job more difficult too.

The ToI piece on 2nd November 2020: https://timesofindia.indiatimes.com/india/timestopten/msid-78984698,card-78984959.cms?utm_source=newsletter&utm_medium=email&utm_campaign=timestop10_daily_newsletter

There is another area where the optimum size is important. MOOCs have been a recent phenomenon and even India has created its own SWAYAM. If we do move towards remote e-learning, an interesting question arises that what is the right size of a remote classroom. From a technological perspective, we could be teaching millions simultaneously with a MOOC. But teaching is different from mere broadcasting. And thus the question arises as to what is the optimum number of a taught learning cohort. Yes it can surely be more than the 20 or so in a traditional classroom. 

Dunbar’s number is a suggested cognitive limit to the number of people with whom one can maintain stable social relationships—relationships in which an individual ( the teacher) knows who each person is and how each person relates to every other person. This number was first proposed in the 1990s by British anthropologist Robin Dunbar, who found a correlation between primate brain size and average social group size. By using the average human brain size and extrapolating from the results of primates, he proposed that humans can comfortably maintain 150 stable relationships. Dunbar explained it informally as “the number of people you would not feel embarrassed about joining uninvited for a drink if you happened to bump into them in a bar.”

Proponents assert that numbers larger than this generally require more restrictive rules, laws, and enforced norms to maintain a stable cohesive group. It has been proposed to lie between 100 and 250, with a commonly used value of 150.

I have been using WhatsApp for teaching for some time now. When I started using WhatsApp for teaching, the limit to membership of a WhatsApp group was 100. Now it is raised to 256. A group that is aligned to the Dunbar number. 

Towards the end, I want to share with you some useful practical knowledge on the right size of digital content. The ideal length of online content : https://buffer.com/library/the-ideal-length-of-everything-online-according-to-science/. The optimal length of all social media update : https://buffer.com/library/optimal-length-social-media/

From the above articles, I have culled out key insights on what lengths are considered optimum for different social media posts. But these are averages. N a specific context, longer lengths may not only be acceptable, but also desirable. 

  • The optimal length of a tweet — 71 to 100 characters
  • The optimal length of a Facebook post – 40 characters
  • The optimal length of a Google+ headline – 60 characters maximum
  • The optimal width of a paragraph – 40 to 55 characters
  • The optimal length of a domain name – 8 characters
  • The optimal length of a hashtag – 6 characters
  • The optimal length of an email subject line – 28 to 39 characters
  • The optimal length of an SEO title tag – 55 characters
  • The optimal length of a blog headline – 6 words
  • The optimal length of a LinkedIn post – 25 words
  • The optimal length of a blogpost – 1,600 words. 7-minute posts capture the most total reading time on average.
  • The optimal length of a YouTube video – 3 minutes
  • The optimal length of a podcast – 22 minutes
  • The optimal length of a presentation – 18 minutes
  • The optimal length of a SlideShare – 61 slides. Unlike YouTube, where shorter content tends to be more successful, SlideShare users welcome comprehensive content
  • The optimal size of a Pinterest image – 735px by 1102px
  • Beyond the data, there is a bit of opposite advice that many hold as a best practice: Guy Kawasaki’s 10/2/30 rule. It says 10 Slides/ 20 Minutes and 30 Point Font

As you will see if you read through the article, these are inferences from the data of people perusing the content. It may have nothing to do with the effectiveness of the content or content targetted at and meant for a specific group. 

What is the optimal length of a single lecture ?

Since the founding of Western universities in the middle of the 11th century, the lecture has been the traditional means of passing on knowledge. Indeed, the 50-min lecture still holds sway at many institutions. Despite nearly a millennium of usage, the established lecture format has come under more and more scrutiny. It is criticized as being too long to hold a student’s attention based on several authors’ claims that a student’s attention span declines precipitously after 10–15 min. Such observations would support the TED approach of an 18-min limitation.

The current standard length of a lecture period is 60 minutes, sometimes comprised of 60 minutes speaking and other times approximately 45 to 50 minutes speaking and a 15 or 10 minute time slot for question-answers. 

It depends partly on the audience, but above about 20 adults, you usually get the same mix of learning styles/temperaments, so it ends up being much more strongly dependent on the lecturer and his/her style. 

Duration of a WhatsApp learning session:

While there are several pieces on the Internet on what is the optimum size of a blog, tweet, e-mail heading, e-mail length, a video talk ( TED with its 20 minute duration has emerged as a standard), there is none for the length of a WhatsApp course.

I have given a few WhatsApp live sessions of 60minutes, 90 minutes and even 3 hours. I feel about 90 minutes is good. 

That is the standard that I propose to use for my Whatsapp talks. 

Incidentally, while doing the research on this, I found that the average length of a Hollywood popular movie was 101 minutes.  So, there is a good chance that  one of these Whatsapp live courses may become a Blockbuster. 

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The idea of Justice

The idea of justice?

 The Constitution of India begins with the promise  “ to secure to all its citizens: JUSTICE, social, economic and political…….” and this clearly indicates that the idea of justice is multi-dimensional and complex. 

The idea of justice changes with geography and time. Einstein would have appreciated the statement that justice is a function of space-time. 

The Manusmṛiti (मनुस्मृति), is an ancient legal text.  It was one of the first Sanskrit texts to have been translated into English in 1776, by Sir William Jones and was used to formulate the Hindu Law by the British colonial government.

It  is variously dated to be from the 2nd century BCE to 3rd century CE, and it presents itself as a discourse on topics such as duties, rights, laws, conduct, virtues and others. The text’s fame spread outside Bharat (India), long before the colonial era. The medieval era Buddhistic law of Myanmar and Thailand are also ascribed to Manu, and the text influenced past Hindu kingdoms in Cambodia and Indonesia ( https://en.wikipedia.org/wiki/Manusmriti).

The Code of Hammurabi is a well-preserved Babylonian code of law of ancient Mesopotamia dated to about 1754 BC. It is one of the oldest deciphered writings of significant length in the world. The sixth Babylonian king, Hammurabi enacted the code. Code of Hammurabi : https://en.wikipedia.org/wiki/Code_of_Hammurabi

Today, approximately 282 laws from Hammurabi’s Code are known. Each law is written in two parts: A specific situation or case is outlined, then a corresponding decision is given.

One of the best known laws from Hammurabi’s code was:

Ex. Law #196: “If a man destroy the eye of another man, they shall destroy his eye. If one break a man’s bone, they shall break his bone. If one destroy the eye of a freeman or break the bone of a freeman he shall pay one gold mina. If one destroy the eye of a man’s slave or break a bone of a man’s slave he shall pay one-half his price.”

Well, Mahatma Gandhi wasn’t on board with that. His quote “an eye for an eye makes the whole world blind” is saying that if we keep punishing those we deem cruel, then we’re no better than the bad guys ourselves. It’s the whole “you can’t solve violence with violence” spiel. Many civilised countries have abolished the death penalty, but blood thirsty societies still crave lynching by mobs and ‘encounters’ by the police. 

Sir Henry James Sumner Maine,  is famous for the thesis outlined in his book “ Ancient Law” that law and society developed “from status to contract.” According to the thesis, in the ancient world individuals were tightly bound by status to traditional groups, while in the modern one, in which individuals are viewed as autonomous agents, they are free to make contracts and form associations with whomever they choose. 

The Idea of Justice is a 2009 book by the Nobel Prize winning economist Amartya Sen. The work is a critique and revision of the philosopher John Rawl’s “ A theory of justice”  (1971). In the book, Sen makes a radical break with the traditional notion of homo economicus, or ‘rational economic man’ as motivated mainly by self-interest. He points out that children have strong notions of fairness and acute aversion to manifest injustice. In his introduction to The Idea of Justice, Sen states that “the strong perception of manifest injustice applies to adult human beings as well (as children). What moves us, reasonably enough, is not the realization that the world falls short of being completely just – which few of us expect – but that there are clearly remediable injustices around us which we want to eliminate.”

One of Sen’s main arguments is that the project of social justice should not be evaluated in binary terms, as either achieved or not. Rather, he claims that justice should be understood as existing to a matter of degree, and should correspondingly be evaluated along a continuum. Furthermore, he argues that we do not need a fully established abstract ideal of justice to evaluate the fairness of different institutions. He claims that we can meaningfully compare the level of justice in two institutions without positing an ideal, transcendental idea of justice. 

Yuval Noah Harari’s “Sapiens” is one of those uniquely breathtaking books that comes along but rarely. It’s broad, but scientific. It seems to be influenced by  Jared Diamond, author of Guns, Germs and Steel and other similarly broad-yet-scientific works with vast synthesis and explanatory power.

One of the reasons why Homo Sapiens dominate the earth is according to Haraki, their ability to imagine intangible ideas such as the idea of human rights. 

In the present digital age driven by Artificial Intelligence, the role of algorithms is an essential element of the applications of the concept of justice. One of the challenges in delivering justice is to distribute a scarce resource to a large population. One of this is going to come up very soon with the availability of the Covid 19 vaccine. Eventually it will be an algorithm that will used for this. 

In the mid nineteen eighties, I had tried to raise the question of whether the random number calculation algorithm of Maruti allocation of cars was violative of Articles 14 of the Constitution of India. 

Weapons of Math Destruction is a 2016 American book written by the Mathematician Cathy O’Neil about the societal impact of algorithms. It explores how some big data algorithms are increasingly used in ways that reinforce preexisting inequality.  

O’Neil, a mathematician, analyses how the use of big data and algorithms in a variety of fields, including insurance, advertising, education, and policing, can lead to decisions that harm the poor, reinforce  racism, and amplify inequality. 

Most troubling, they reinforce discrimination: If a poor student can’t get a loan because a lending model deems him too risky (by virtue of his zip code), he’s then cut off from the kind of education that could pull him out of poverty, and a vicious spiral ensues. Models are propping up the lucky and punishing the downtrodden, creating a “toxic cocktail for democracy.”

She posits that these problematic mathematical tools share three key features: they are opaque, unregulated, and difficult to contest. They are also scalable, thereby amplifying any inherent biases to affect increasingly larger populations.

This year the 2020 Nobel Prize in Economics was awarded for the model /theory of auctions ….. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2020 was awarded jointly to Paul R. Milgrom and Robert B. Wilson “for improvements to auction theory and inventions of new auction formats.” 

We may expect innovations in democratic processes beginning with choosing the population’s representatives and the algorithms of collective opinions and decision making. 

Algorithms that are ‘just’ is the next step in the ‘idea’ of justice. 

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