It has been a while since I last blogged, now perhaps it is fitting to go back to talking about education.
Let’s home in on an important issue in education: creativity.
Or to be more exact, how ought we reform education to make it more conducive to creative activities?
Over the last couple of months I picked up a couple of books that I thought provide a good survey of the theories and the issues behind creativity, here are a couple of good ones:
- How Learning Works by Susan Ambrose et al. – A good survey of learning science concepts such as knowledge, motivation, and metacognition to help us understand how learning happens.
- Most Likely to Succeed by Tony Wagner and Ted Dintersmith – Provides an overview of problems and challenges with a testing-driven education system
- That Used to be US by by Thomas L. Friedman and Michael Mandelbaum – Discusses growing economic challenges in the U.S primarily due to an inadequate labor force
- Change.edu: Rebooting for the New Talent Economy by Andrew Rosen – Discusses challenges in education and in the U.S economy that call for reform in our education systems in order for the country’s labor force to stay competitive
as well as the following paper:
- Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching by Kirschner, Sweller and Clark – Explores the effectiveness (or lack thereof) of pure constructivist and discovery learning methods as pedagogical methods
If you’re short on time, simply skim through the bold-faced items.
There is no doubt that fostering creativity through formal education is a theme, and many good (albeit contentious) points have been raised in writing. Despite all the effort, I have always felt that certain narratives are left out of the mainstream conversation due to their lack of shine and luster compared to project-based learning or computer science education. Much of that has been left out, I believe, is filtered because “creativity thrives on robust content learning” is never an attractive slogan for a reform.
I want to take this chance to contribute to this conversation by exploring additional narratives that we should consider in designing pedagogy, educational technology solutions and educational policies.
The missing case studies
The first thing I want to survey is the imbalance of case studies (or anecdotes, really) that pervade books on education and educational reform. By that I mean: we have plenty of case studies of 1) students who perform well in the current test-driven K-12 system and prosper, 2) students who performed well in the current system but ceased to perform in higher ed, or in the job market, 3) students who performed poorly in the current system but succeeded in learning on their own (autodidacts, if you will), and then performed well either in higher ed, or in the job market.
These stories have been regurgitated over and over, and it’s obvious that at least two archetypes of learners have been left out:
4) students who performed poorly in and outside the formal K-12 education system, and did not do well beyond that, as well as 5) students who performed poorly in the current system but succeeded in learning on their own (autodidacts, if you will), and then performed poorly in higher ed and in the job market
It is somewhat understandable why these two archetypes are left out: #4 is demotivating, and #5 is outright anti-climatic.
But the truth is that, #5 is an important and nontrivial case, that very few who talk about education want to acknowledge.
We want to believe that autodidacts are great role models for our pedagogy and they will save the world, but we generally refuse to acknowledge that a significant portion of autodidacts will be unable to tackle many challenges both in life and on the job, due to their isolated style of learning.
I am an autodidact to a large extent, inasmuch as I started teaching myself programming when I was twelve, with a couple of books and a dial-up internet connection. This was the late nineties. I grew more interested in programming because I got wholesomely burnt out by the monotony of compulsory education, which ran pipelines that fed middle-schoolers into high-schools and SAT preps, then eventually funnel into colleges. Programming became more attractive as I discover ways to learn and to create flexibly, without a prescription or a mandate. Through the internet, I met many developers, most were in their twenties and a few were my age. This loose online association of autodidacts was a fascinating gateway to creativity, that for a bored teen like myself then, was a life-changing experience.
Eventually programming lead me to regain my interest in academic subjects such as physics, mathematics and even ones farther removed like literature, as I have learned to see content learning as a foundation to creativity. Later I was admitted to Carnegie Mellon, studied computer science and philosophy, did some research then eventually started my own company.
On the other hand, the absolute majority of the autodidacts I encountered in my teens never turned out the same way. Those in their twenties and thirties, started out as IT consultants and remained consultants. Those who were teens like me, failed to gain admission to an engineering program in college. A once close friend of mine, an autodidact who taught himself half a dozen programming languages and frameworks, a brilliant hacker who wrote scripts to crack passwords and to probe for security loopholes, got burnt out with computer science because the lack of adequate schooling in mathematics. He eventually dropped out and became an IT consultant, and was stuck doing that.
I am an IT consultant for a couple of years while in school and before starting my company, and it is no surprise that in IT consulting the day-to-day work involve “creations” that come out of cookie-cutter templates (e.g. setting up wordpress, creating a questionnaire, setting up a server, etc) that lack creativity in a sophisticated sense. It is different from the type of software engineering you expect at tech startups and R&D firms that require deep scientific knowledge and problem-solving expertise. In fact, IT consulting is the “development” work that has been increasingly automated, or moved offshore to developing countries with cheaper mid-skilled labor.
The real point of failure wasn’t creativity, it was the lack of robust content learning and effort-regulation that imposed a ceiling on many self-taught developers who never received formal engineering training. After a certain point, continuous fiddling with code won’t lead to effective solutions to nontrivial problems, one will need to acquire theoretical knowledge (like symbolic systems) as well as better learning strategies in order to comprehend and design solutions that involve complex systems.
Practical autodidacts do not lack project-based learning experience, as most of what they do are usually grounded in some sort of real-life problem-solving. What they generally lack is efficient conceptual frameworks to organize thoughts at an abstract level. The problem isn’t that autodidacts can’t teach themselves complex conceptual frameworks, such as various symbolic systems (e.g. differential calculus, predicate logic, combinatorics, etc), but the fact that complex thoughts are often (at surface value) divorced from practical problem-solving, relatively few autodidacts have the patience to regulate their efforts to learn these boring and useless topics. Unfortunately, most meaningful innovations in technology and discoveries in science, require higher level conceptual frameworks to progress. Content learning of these seemingly useless (as referred to in many publications) topics such as calculus, economics, discrete mathematics, game theory, etc, turned out to matter a lot for creativity at a higher, and much more sophisticated level.
The point here is not to disparage any form of discovery or project-based learning, but to understand the difference in the aims of the teaching of creativity vs. the teaching of knowledge, skill and facts.
The case of vacuous creativity
One of the biggest myths that gets perpetuated just a little bit too much, is the myth that children are born creative geniuses and education murdered them. Similar things have been said in the reading I recommended in this post, and has been echoed in many places, including the famed TED talk by Sir Ken Robinson.
Sure, there is probably some truism in this, but there is also an obvious paradox that we enjoy ignoring:
It seems like the most creative and productive members of our society, are also simultaneously the ones who are the most educated and most proficient in content learning.
In simpler terms, many if not most of our top innovators are also the ones who remember their multiplication tables, who remember when the US was founded, who remember the quadratic formula, and who remember the names of the Greek architectural traditions. Just about everything that many call useless and impractical and worst of all, uncreative.
Our best researchers, best liberal arts students and even those who were persuaded to choose a career as opposed to college (like those in the Thiel fellowship), are also the ones who read the most and know the most compared to the rest of the population.
It then doesn’t seem to make sense to think that content learning in our education system is what is killing creativity, simply by the law of contraposition, which of course, probably belongs to the set of topics that education reformers deem useless and impractical for a student to learn.
The truth is that vacuous creativity is not what we are aiming for. We often refer to kids’ drawings and spontaneous creations as evidence of creativity that we no longer possess when we enter adulthood, but in reality, the type vacuous creativity that we entertain during childhood, is not conducive to creative productivity in the real world. A child drawing a plane, is very different from an aerospace engineer designing the next commercial turbojet. Just because I drew a castle in kindergarten doesn’t mean I would have (or even could have) become an architect or a civil engineer. To do so is akin to judging a person’s business acumen based on how one performs in a game of monopoly.
We eventually lose interest in vacuous creativity because it isn’t productive or “worth the time” in many cases; and at the same time very few of us gain the ability to create productively, because it is difficult and arduous.
Our aim in reforming education is not to retain vacuous creativity, but instead, teach what is necessary to develop productive creativity, and while motivation and metacognition play big roles in this process, so does content learning.
We’re not talking about this that often because it is not the most “sexy” thing to say, it lacks the “wow” factor. But it is something that must be done, and in order to stay focused, we need to stop peddling vacuous creativity as anecdotal evidence.
The notion of school as a pipeline into the job market
The next topic of discussion that caught my attention is the overwhelming emphasis on how our school systems don’t teach practical skills and how 21st century labor requirements have out-paced our 19th/20th-century education system.
Sure, there is again, some truism to this statement, but the question is – why do we need education is about teaching practical skills for a job?
This is premise that many ancient Greek philosophers would protest, the Renaissance philosophers would protest, and even Confucian scholars would protest (in fact, Confucian Analect repeatedly emphasized the importance of fostering critical thinkers through learning and education).
Even today, our nation’s top universities are not purely technical, our best engineers have to be adequately versed in the sciences and the arts. Moreover, our nation has a proud tradition of liberal arts colleges that breed some of the most sophisticated thinkers, innovators and artists in modern history.
And if one seeks to garner support with engineers and autodidacts, the truth is that good engineering schools teach computer science and other sciences, as opposed to programming languages, programming frameworks or IDEs (Integrated Development Environments), is because to design an intelligent software system that solve a difficult real-world problem, it is important to know statistics, psychology, calculus, and logic, than to know Ruby on Rails, or iOS/Android development.
As far as our leading higher ed institutions are concerned, the goal of education is to teach conceptual frameworks used to model and solve problems, and not to simply learn to use a tool that exist solely as a contingency of the modern era. Tools will become obsolete, but analytical abilities won’t.
While it is understandable why it is beneficial to have educational institutions that graduate qualify workers for corporations and organizations alike, it is unfair to judge education solely on the graduates’ immediate employability for technical jobs, while sacrificing long-term benefits of analytical and creative capacities that result from scientific and artistic studies. (Again what many have labelled as useless and irrelevant)
One should keep in mind that entrepreneurs are generally not immediately employable, but these individuals are ones who have the ability to learn fast, adapt to change and integrate diverse skillsets to create exciting new ventures.
If we only learn what we can get paid to do, we will never really learn to think.
A computational analogy for creativity
Before I conclude the post, I want to take a minute to talk about a computational analogy for creativity.
Prior, I spoke of the danger in relating vacuous creativity to creativity with productive value. Then we must ask – what is creativity and how are creative activities carried out?
I personally see creativity as the ability to discover original, previously undiscovered (relative an immediate environment) possibilities that have value.
Which, in terms of cognitive science and artificial intelligence, it is the ability to perform search well.
When we work to create, we are working to discover a new possible solution to a problem.
Think of an aerospace engineer designing the next air-superiority fighter, or an artificial intelligence like Deep Blue or AlphaGo, looking for the best move to beat the opponent, there is always a vast space of possibilities. In the worst case scenario, the individual conducting the search goes about by brute-force, and try all possibilities.
Brute-force, this is essentially what an infant does, and it is essentially what is wrong with vacuous creativity.
If we go about creating by pure trial-and-error, most of us will run out of time (i.e. life) before we discover an idea of value. The true difference between an aerospace engineer and a five-year-old with a crayon, is that the aerospace engineer needs to be versed in fluid dynamics, propulsion, electromagnetism, and many other scientific topics in order to filter out the absolute majority of the possibilities that don’t work, and focus on a tiny minority of ideas that have scientific merit. Similarly, when an artificial intelligence seeks to beat a human player in a game, it is active filtering out the absolute majority of possibilities that don’t work, and focus on figuring out which of the tiny monitory of possibilities is most likely to lead to victory. For AI, the possibilities are often filtered based on some heuristics learned from data of how human experts play the game.
What this tells us is that extensive and robust background knowledge is not only helpful, it is necessary to be productively creative.
Again, the point is not to say some sort of project-based or discovery learning is not important, but rather, this is a call for a more balanced approach to thinking about how to promote creativity through education: promoting critical thinking, motivation and meta-cognition should not be thought of as replacements for content learning, instead, we should aim to innovative methods that promote creativity, while preserving and improving effective content learning methods.
It is an exciting topic, more to come 🙂