Tuesday, July 9, 2013

Accelerating Learning: Is it possible to beat 10,000-hour rule?

In his very well-written and popular book Outliers, Malcolm Gladwell popularized 10,000-hour rule. This rule is based on work done by Ericson, a psychologist.  The basic premise behind this rule is that it takes 10,000 hours of quality practice to become an expert in something. Another wonderfully written book Bounce by Mathew Syed also referred to this rule. Both books attempt to explain anatomy of success, in particular, extreme success. 10,000-hour rule has been interpreted by the popular media in many different ways. You might disagree with the numerical value of the number of hours it takes to become an expert. However, there appears be no doubt that currently it takes a long time to become an expert.

We are living in the age of rapid technological advances. The rapid change of technology is a harbinger of creative destruction. As an existing industry dies due to obsolescence of the underlying technology, many jobs associated with it disappear too. Similarly, the birth of a new industry creates many new jobs. We will soon be approaching the situation where people will need to retool themselves by acquiring new skills every five to ten years to ensure that they remain employed.      

This new reality is in conflict with the way education system works today.  To become expert at something and get a well-paying job, one must spend years in post-secondary training.  If you want to change your field significantly, you can count on spending several years in the school again. 10,000-hour rule seem to provide a justification for it! However, spending years in school to retool themselves after losing the job is not going to be an economically viable option for most people.

We need to find a better way. One way would be to accelerate the learning process.  Can we beat 10,000-hour rule? Can we master a new craft in 1,000 hours instead? 
In a conventional classroom, one memorizes lots of facts and information, develops motor skills necessary to do the physical tasks associated with the profession (e.g., surgery), and learns problem solving and decision making skills. In disciplines that involve creating something new (e.g., engineering design, architecture), one also learns synthesis process and divergent thinking to enable creation of new artifacts.        

We live a different world compared to the early twentieth century. However, we have not made any significant leap in the learning process over the past one hundred years. I would like to share the following observations:

  • A large amount of time in a conventional education program is spent on memorizing lots of facts and information.  Clearly, it was necessary to do it in the past. But within few years, we can envision a smartphone that gives a person ability to instantly search for virtually every known fact and information.  How crucial is it to devote time to memorizing all the facts associated with a profession? We can instead imagine a scenario where a human memorizes crucial high level facts that help him/her in understanding how the information is organized within the field, but the low level facts need not be stored in the human brain. The human can access them from the cloud on as-needed basis. The decreased emphasize on rote memorization can speed up the learning process.
     
  • In many educational programs, a significant amount of time is spent on motor skill development.  Many future jobs will be done with assistance from robots (and perhaps exoskeleton). This should reduce the time needed to develop motor skills.    
     
  • In the current education system, problem solving, decision making, synthesis, and divergent thinking skills are learned in the context of a discipline.  So these skills are not easily transferable to a new discipline. For example, let us assume that you are currently an architect and would like to switch to bio-medical engineering. Unfortunately, it will take you many years in school to accomplish this.  We ought to be able to structure education such that problem solving, decision making, synthesis, and divergent thinking skills are learned in such a way that they can be easily transferable from one career (e.g., architect) to another (e.g., bio-medical engineer).   
     
  • Technology can be used during the learning process to ensure every hour spent on learning actually contributes to learning. Facial expression recognition (and perhaps non-invasive brain imaging) can help in making sure that the person is not getting bored or frustrated! This ought to improve the learning process. Personalized computer-based tutoring system should also improve the efficiency of learning process.  
In my opinion, accelerating the pace of learning is one of the biggest challenge and opportunity facing the human race.  Clearly, training world-class athletes and musicians will continue to take more than 10,000-hour of quality practice. But we ought to be able accelerate learning in many other fields.

5 comments:

  1. missing an important thing abt how brain generates ideas! all problem solving, decision making, synthesis, and divergent thinking skills follows after brain generate ideas ... getting ideas, brain needs systematically stored data... as much as you have data that much good ideas brain could generate.. so memory in conventional education system is very important....what do you say?

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  2. Interesting article. I agree with you. We dont need to memorize a formula to solve a problem rather we need to know how to use the formula. Unfortunately, a large amount of time in current education system is spent in memorizing the formula. That perhaps makes the teaching/grading easier but does not contribute much to the learning.

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  3. We need to find a better way. One way would be to accelerate the learning process. Can we beat 10,000-hour rule? Can we master a new craft in 1,000 hours instead?

    In a conventional classroom, one memorizes lots of facts and information, develops motor skills necessary to do the physical tasks associated with the profession (e.g., surgery), and learns problem solving and decision making skills. In disciplines that involve creating something new (e.g., engineering design, architecture), one also learns synthesis process and divergent thinking to enable creation of new artifacts.
    This is a really good topic and set of questions. It impacts much in the educational arena and in what undoubtedly will emerge in evolving learning algorithms and machine intelligence.
    Does it really HAVE to take 10,000 hours (10KH) to become an expert? I doubt it. I agree that the ‘conventional wisdom’ and the anecdotal data support the 10KH range of dedicated time investment. But I also believe that many of those 10KH are associated with hit-or miss approaches. There are things one learns very quickly (e.g. only one experience with a hot stove makes you an expert in not touching a hot stove or putting your hands into a fire, also it takes less than 10KH to be a pretty good bike rider, and this involves a lot of balance and coordination).
    Technology certainly has a tremendous role to play. For example high tech biofeedback techniques, used by NASA for astronaut training, can train individuals in rather short durations to achieve significant, tangible, measurable body control that might take many years of meditation and other types of training for others
    I think one must examine several aspects carefully here.

    [continued in next post - as the total exceeds 4K char)

    Elan

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  4. [Continued from IR previous comment]

    I think one must examine several aspects carefully here.
    a) The suitability of the ‘learner’ to the material being learned. There is a distribution of talents and skills, and folks with less aptitude will take a longer amount of time to learn the same skills, develop proficiency, and progress to expertise. Of course there’s also the matter of motivation.
    b) There is the distribution of talent and skills of the “teachers/mentors”. Same story here, teachers and mentors vary considerably. While the most gifted and motivated may become experts and masters independent of the teachers and mentors; these individuals will arrive at extreme proficiency much earlier under tutelage by true experts.
    c) There’s the aspect of ‘learning how to learn’; there are individuals who have told me that they really didn’t learn how to learn efficiently until they were seniors in college. These individuals would have gained much more by having really good techniques (algorithms) of how to learn.
    d) There’s the aspect of actually how much new material is learned, how many times material is practiced or needs to be practiced. Yes, indeed the more you practice the more you develop an understanding of what actually you are doing, and if there are variations or more efficient approaches. So the more time you have to ‘explore’ the ‘variation/alternatives space’ the more you become aware of optimal approaches.
    e) There’s the neurological aspects of learning and memory. It’s not all clear that learning strategy takes that into account explicitly. The Hebbian neural reinforcement, the conversion from short term memory to long term memory, the consolidation of memory during sleep are all factors, but to my knowledge there’s no systematic understanding of what works better for different types of skills. Also within the neural domain; different brains are wired differently.
    f) There’s also the matter that different expertise areas require different amount of knowledge. How does one quantify what knowledge is required to be an expert or master and how does one quantify it? Its easier to be an expert in a totally new field than an expert in a long established field.
    This is a topic that deserves extensive discussion. Clearly U.S. and world investment in education is rather significant, and being able to educate more effectively. It may be of even more significance if we can improve the approach to reducing the time to function at an expert level from 10KH to 1KH as SK suggests.

    Elan

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  5. This article touches on a number of rather deep philosophical issues. The general theme seems to be "can the human brain keep up with accelerating technological advancement?" There are a number of directions we can go from here.

    On the one hand we can say yes, our technology is outpacing the capabilities of our brains in some fundamental way. With this assumption we might respond by saying that we need to "upgrade" our brains directly, whether by cognitive implants, genetic engineering, nootropic drugs, or other futurist/transhumanist concepts. Besides the ethical and social implications of such an approach, our understanding of how the brain processes information is still limited, and it may be a long time before this is even technically feasible.

    Of course, as SK and several others pointed out, we can already "augment" our brains through laptops, smartphones, VR training, and Google Glasses. For rote tasks, these can certainly allow workers to get up to speed faster by turning some complex algorithms and computations into a black box. They can also allow us to find new information faster. However, there is still the question of whether these technologies can speed up creative processes any faster than say, a book.

    Another possibility is, as SK brings up, that this is entirely a pedagogical issue. I have observed that in recent years, the educational system has increasingly shifted to emphasize vocational skills, rather than general intellectual refinement. In my experience as a math tutor, I find myself constantly being asked "why do I have to learn calculus? I'm going to be a businessperson/marketing specialist/policy specialist!" I respond by explaining that it's the different ways of thinking that calculus exposes them to that is important, not the details of learning how to differentiate and integrate. However, colleges have taken these complaints seriously, and I'm finding much of the rigor of calculus, including limit theory, is being removed and students are coming out with only a mechanical understanding of the math.

    However, I am not sure that this means we can just abstract away the domain-specific context of teaching students "how to learn." Most of us need concrete examples in order to learn. This is why we start by learning the specific theories, such as Green's Theorem, before their more general/abstract counterparts, such as Stoke's Theorem. Even the historical order of discovery tends to go from the specific, concrete cases to the general/abstract/unified case. This should give us a hint as to how the creative process works.

    I think it is important that students select a specific field of study, but that a healthy mixture of theory and application are offered. Students should start with a robust seed of knowledge and concrete facts, but still be able to grow and adapt to a changing technological landscape.

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