Thursday, January 11, 2018

Meet an Astronaut and help Science Education!

The Science Club at Northbridge High School is sponsoring a fundraising dinner on Friday, February 23, 2018, to benefit their NASA ICED Epic Challenge Program. The keynote speaker will be Dr. Charles Camarda who saved the space shuttle program with a team of his friends by designing and testing a patch to the space shuttle heat shield that could be deployed in Space which inspired him to create the Epic Education Foundation. Dr. Camarda is an astronaut, Sr. Advisor for Engineering Development at NASA Langley Research Center, Developer of Innovative Conceptual Engineering Design (ICED) methodology, and Founder of the Epic Education Foundation. See for more information & to purchase tickets.

Saturday, January 6, 2018

On the roots of the behavioral economics.

Fact: people “don’t always make rational decisions in their best interests.
Question, “Why do they do that?” is explained in the book “Nudge: Improving Decisions about Health, Wealth and Happiness”, by Richard Thaler and Cass Sunstein.
The book is a strong source of very good insights, ideas and strategies. But it is rather large.
However, there is a shortcut helping to see the general idea behind the behavioral rules in economics.
This shortcut, though, has one limit; not everyone can use it, but only people with a special experience.
That special experience is called “parenting”, and those people are called “parents”!
Parents know very well how difficult it is to manipulate a child into doing something he or she does not want to do.
Parents know very well how often a child tries to do something which might do him or her some harm, even when the child “know” so, and how difficult is to make to agree the child to stop.
Child psychologists have developed many strategies for parents helping them to “trick” a child into making the right session (of course, from the parents’ point of view; but leaving the force to the last resource).
For example, such strategies include “bribing”, or “threatening to take away something valuable”.
BTW: most kids feel loss stronger than gain, even if the loss or gain would be the same, like the same toy. That happens because loosing something one has known for a long time is indeed not the same as getting a gift of something one sees the first time.
Hence, “the theory of loss aversion”. 
The book offers similar strategies, but modified for adults.
And the reason for those strategies to work is because many adults, even the smartest ones, often act like children.
 One can ask: “Why would smart adults act like children?”
The short answer is “Because no one can know everything.”
When people grow up and learn new things they get more and more professionally proficient in some of the areas of human practice. But even the smartest and the most knowledgeable person has his or her limits. When those limits are reached and a person needs to make a decision at the edge of his or her knowledge and experience, that person often starts acting in the same way he or she was acting when a similar situation happened with him or her the first time, i.e. in the far past, i.e. when the person was a child. In that state of mind a “nudge” which worked when the person was a child has a high probability to work again.
When that happens, telling the person “stop acting like a child and be an adult” is usually useless, or at least less helpful than finding the right “nudge”.
Hence, the behavioral economics.
Hence, the book (a good one!). 
Hence, political and economic “nudge” ideas. 
According to the “Nudge”: “people tend to be somewhat mindless, passive decision makers.” As a teacher who taught very many students how to make decisions (thousands of students; and not just via one-way lecturing, but having many face-to-face conversations) I agree with this assessment.
However, I would not use term “decision making” to describe a “passive mental state”, because “making” does not sound passive to me.
I would call people in such a state of mind “passive choice selectors”.
This, of course, is based on the answer to a question: “How do people make decisions?”
For example, when you (or your child, or your friend, or your enemy) stand in Baskin Robbins (or Dunkin Donuts, or Burger King) staring at the menu trying to figure out what to order this time – do you make a decision, or you just select one from several choices?
The answer to this question depends, in turn, on what do you call a “decision”.
Without dipping into a long discussion, let’s just say that in general, we – humans – make two types of decisions: rational decisions (a.k.a. logical, via a step­-by­-step reasoning; “true decisions”, or just “decisions”), and “irrational” (a.k.a. intuitive, a.k.a. a guess, or a hunch; which should be called differently). The ability to make rational decisions differs humans from other animals; take this ability away – and we will be no smarter than dolphins, or dogs, or monkeys, or cats.
During a usual day, we do not make too many of actual decisions (a.k.a. “rational decisions”, a.k.a. decisions), because usually we do not have to. Usually, we just have to select one from several choices which do not greatly affect our well­-being, or our future.
When we are angry, or when we are happy, or when we are sad, or when we are stressed, we may not select the same choice which we would have selected if we were calm and rational.
The reaction which we call a choice, (or many people call it a decision), is the result of the brain functions happening in our subconscious mind without our interference. Maybe, on average, we use about 10 % of our brain power for a logical reasoning. But that does not mean that other 90 % of the brain do nothing. Our brain is constantly analyzing on its own, without our awareness and interference, a huge amount of information to answer one single question: “What to do to survive?” (the strongest instinct of every healthy animal is self-preservation). Then our brain decides for us what choice for our immediate action should we select, makes as to act on that selection, and then places that selection into the logical part of itself, so we would be able to articulate it (first of all – to ourselves). And then we start defending this “decision we just made” like we were actual authors of it.
Of course, the picture I just painted is very simplistic, but presents a good initial model of a human “decision-making” process, which in reality is a “choice selection” process.
And one more note: the stronger our emotions are, or the longer we experience those emotions, the more chance is that we would not listen to any rationalization of our actions. We would tend to just react. There is a study which demonstrates that when people experience stress over a long period of time the brain chemistry changes (that is why doctors prescribe pills to help with depression). Disorientation is not just a psychological state, it is a state of a physical dis-junction in a brain.
When a person, who is on the edge of the knowledge, has to make a decision, it also causes some stress, and may cloud the decision-making process. The result is often slipping from making a decision to selecting a choice.
When the result of our choice selection might greatly affect our future, we tend to become even more stressful, and, hence, even less logical. This is one of the paradoxes of the decision-making process.
This is one of the reasons why people “don’t always make rational decisions in their best interests.

Appendix I:
To get to know me better, I would recommend to check the following web-links (would not take more than 20 minutes of total time): 

Appendix II:
My comment to an interesting paper on the matter

Economics is reaching the state physics has passed at least a century ago, i.e. making a transition from ideal models (pure math based on the assumption that people are robots who always know what is right, and always do what is right), to models which need to include errors and fluctuations. Good job!


Wednesday, December 27, 2017

How much of “cyber” in “cyberlearning” and "cyberthinking"?

How much of “cyber” in “cyberlearning” and "cyberthinking"??

Part I: "Introduction" - why do we need to talk about cyber education (everyone who does not need to be convinced can just skip to part II)  
“Computer based technologies, including robotics and Artificial Intelligence, entering all aspects of society, including private and professional life of millions of people.
That is why our students need to learn how to write a code, how to program, they need to develop cyber thinking via various aspect of cyber learning.”
This statement in many different forms have been circulating for a while. The simple internet search leads to thousands of entries, which usually span between 2010 and current year, for example:

Every week or two some media outlet issues a new article of a video about learning how to code, computer literacy, the importance of the informatics.
For example, The Wall Street Journal has dozens of videos on the matter:
General public is not aware, however, that slogans like “We need to teach students coding”, “Student have to learn informatics” and similar, have been around since the begging of mass production of personal computers, meaning, for about 30 years already.
And not only in America.
In Russia, for example, all university students had to learn how to code using MS Basic and FORTRAN since yearly 1980s (at least). Since late 1990s all high schools in Russia were teaching informatics. Maybe, that is one of the reasons that the match between the U.S. and Russian cyber forces looks like a draw (at least) despite the huge technical and financial advantage of the U.S. (“Why Did Russian Cyber Forces Beat Their U.S. Adversaries in 2016?”;
BTW: If everything goes according to a plan, in the near future all Russian schools will also be teaching game of Chess (it looks like they really want to force everyone into thinking).
America has never suffered from having low numbers of American-born students selecting STEM related field, including computer study, because the world always could provide enough qualified foreigners wishing to work in the U.S. This situation, however, may be changing due to changing immigration policies (“What Would Businesses Do if No Foreign Students Could Come In the Country Anymore?”;
This is one of the reasons for reignited urgency for reviving 30-year old slogans. For example, on December 12, 2017, in his Testimony Before the U.S. Senate Committee on Science, Commerce, and Transportation Subcommittee on Communications, Technology, Innovation and the Internet, Vice President AI and IBM Q, Dr. Dario Gil said: “There are actions we must take now to ensure the workforce is prepared to embrace the era of AI … we must address the shortage of workers with the skills needed to make advances in AI … We need to match skills education and training with the actual skills that will be required in the emerging age of AI … We can use the example of the adoption of software programming as a critical skill that is taught in many high school and colleges. Some colleges require that all students learn how to code since they consider it a necessary skill for success. Students becoming proficient in programming have a wider range of job opportunities.” (

Part II: Pedagogy of cyber education
However, the current pedagogical approach to advancing cyberlearning is based on insufficient methodology.
According to a common definition (

Coding is essentially matching something, which was classified or identified with a code.
“Classified” or “identified” usually means a set of actions which need to be performed in a specific order under specific conditions, and is usually called “an algorithm”.
“A code” represents a set of symbols, which represent specific operations over specific objects with specific properties.
The process of coding is essentially composed of two independent sub-processes: (1) development of the algorithm; and (2) assigning a symbolic code (a command) to each element of the algorithm.
General public usually makes an equivalence between “coding” and “assigning a symbolic code to each element of the algorithm”. As the result, “learning how to code” (and cyberlearning in general) is shrank to “learning a code”, i.e. memorizing symbolic representation of various commands.
In reality, memorizing coding commands without being able to produce a workable algorithm is like memorizing the meaning the individual words of a foreign language, but not knowing the grammar, hence not being able to produce a meaningful sentence.
An ability to develop a workable algorithm is the central and the most important ability of coding, programming, and computational thinking in general.
In order to be as efficient as possible, the process of cyberlearning should be based on already developed ability of developing workable algorithms.
That means, that the development of algorithmic thinking should precede the development of computational thinking.
Say a name of any device which comes to mind.
A phone.
A TV set.
A gas station pump.
Any device!
They ALL – all devices in the world – have been designed using algorithmic thinking.
Every single technological process – from the first assembly line to the Amazon warehouse and shipping facility – also has been designed using algorithmic thinking.
Designing the launch protocol for a shuttle or a rocket, or a recipe for a meal, or a plan for a wedding is impossible without using
algorithmic thinking.
And, of course, every single code written to operate any device or a process, from a TV remote control to the blockchain technology, has been written using algorithmic thinking.
In general, designing a device, or a protocol, or a process, or a program which includes a set of actions which are distributed in time is impossible without the use of algorithmic thinking.
Cyber thinking represents a small part the algorithmic thinking and just cannot be developed without having developed a sufficient level of the algorithmic thinking.
But the development and advancement of the algorithmic thinking does NOT require any reliance on computer programming, and computers in general. In fact, learning how to code, or write programs is NOT the best way to development of algorithmic thinking.
Algorithmic thinking can and needs to be advanced outside of the computer, or programming, or coding classes and within a variety of STEM subjects. Two subject fields, which are the most suited for advancing algorithmic thinking, are physics and mathematics.
However, in order for physics, mathematics, or any subject, to be an effective tool for the development and advancement of algorithmic thinking, those subjects must be taught in a specific way; i.e. the development of the algorithmic thinking should be one of the specific objectives of the teacher teaching those subjects (ideally, all teachers teaching STEM subjects).
On how and why physics should be taught to all students: (A) a text,; (B) slides,, slides 59-61 point again at a relationship between cyber thinking and thinking.
Consistency demands to state that the success in study physics, math, chemistry is impossible without preceding success in reading, writing, arithmetic. Too often these days one can read that students do not need to learn handwriting because they can type, or students do not need to know multiplication table (just an example) because they can use a calculator. People who make those statements don’t know anything about leaning; and people who believe in those statements should stop walking because they can drive a car - the same logic!
Finally, we need to stress that the development of the advanced algorithmic thinking is impossible without the development of thinking ability – in general. Expecting the development of the advanced cyber or algorithmic thinking without making sure that the person has the mental and intellectual capabilities required for that, is like expecting that a person who can barely walk would win a Marathon.
Any type of thinking is happening in the brain. Advanced thinking requires advanced brain. As I like to say ( “If the only exercise students had been doing for twelve years is squats, they will not be good at push-ups and pull-ups. Do not expect from students an ability to think if all they had to do for twelve years was memorizing facts and rules”. 
I think the following analogy will be useful for IT professionals. The evolution of the growing brain due to regular exercise is similar to the evolution of a CPU due to engineering advances. 
In order to design an algorithm a person needs to be able to manipulate with a large number of mental objects, mental entities (the complexity of the algorithm is proportional to the required brain power).

That ability is based on another fundamental human ability - imagination! A  general public is used to think that imagination is only important for acting or writing. The fact is that one simply cannot succeed in any STEM related field without having a developed imagination.

Imagination needs to be trained and developed. This type of training requires a special methodology and a specific teaching technique (
But in short, reading and writing is much more useful (hence more important) for the development of imagination than watching TV or playing video games.

A closer look at the current place of AI:

On a definition of AI

The Dawn of The New AI Era.

To get to know me better, I would recommend to check the following three web-links 
(would not take more than 20 minutes of total time):