Education System

In Defense of Data Centrism

In the never ending search to know “what works,” we have a few choices.  We can look to theories, i.e., this ‘should’ work; or we can look to data.  Often the latter choice is considered backward looking, or stripped of context.  Data autopsies are conducted with the results analyzed and presented as a ‘case study.’  Here is what happened with BP, or Enron, or the 111th Congress.

The former choice, relying on theoretical principles, is considered by some more noble and can be expressed simply:  spread democracy, protect the borders, cut taxes, etc.  Theories that are believed to represent fundamental levers of reality - when pushed in a certain direction, desired outcomes result.  These theories are often referred to as ‘common sense.’  Of course, yesterday’s common sense included such principles as racial inequality, ignorance to environmental stewardship, ever-rising housing prices, sexual preference as a preference, etc.  It takes frightening shocks to the system to shake our faith in such levers.

Yet, ever hopeful, we press on to learn what levers control our universe.  What works, and why?  And how can we scale it?  The answer, I believe, comes from data.  But not just any data.  And not through data autopsy and case studies.

Take education.  We can approach improving education as a principled journey, applying common sense: reduce class size, return to single-gender classrooms, dress them up in uniforms, etc.  Or we can turn to data.  Yes, we can do both, but levers must be informed and confirmed by data.  As a friend tells me: The data must precede the framework.

In education, however, we have a paucity of data.  In a conversation with a senior official at the Department of Education last year, I discussed our shared idea of data platforms until he stopped me mid-sentence:  “You’re assuming we have the right data.”  No, I didn’t, but he was right.  I was designing the platform before taking on the fight to tease out relevant data about student performance.

Even that phrase, student performance, is loaded with assumptions.  Performance as measured by what? Standardized tests?  Only last year did the majority of U.S. states agree to a common set of performance standards - and only then applicable to middle-school math and English.  As to how students are assessed against these standards?  That remains in debate, currently there are two clusters of states reviewing approaches to common assessment regimes.  We are years from a U.S. approach to these fundamental levers for K-12 education:  What is the standard against which student performance is measured, and how is that performance measured?  (I should acknowledge a competing theoretical construct that opposes any national approach to education - again, I seek the data here.)

It gets worse.  Institutions of higher education find an increasing number of applicants, year over year, lacking in the skills needed to succeed in their first-year studies.  The resulting ‘remediation’ classes are nothing more an extension of high school.  However, talk with those in the field of education, and they will tell you that K-12 schools have no common tasking from higher education regarding what is considered an acceptable skill set.  While we work to get to a U.S. approach to these fundamental levers for K-12 education, this effort is not coordinated with the expectations of universities and colleges - who themselves do not agree on the answer to that basic question.

It gets worse.  A recent study by IBM surveying CEOs found that the most pressing challenge is the complexity of their enterprise and industry, and the most necessary skill is and will be creativity.  The ability to think critically, understand variables, and make decisions amidst uncertainty.  Meanwhile, the fundamental levers we believe are necessary for K-12 education are descendants of the Trivium - the triad of grammar, logic and rhetoric developed first to shape medieval liberal arts students.   A consortium of technology companies are working to develop the definition of ‘21st century skills’ they believe are necessary for their incoming labor force - but in the radically localized Education field, there is no King to accept their input.  One large government contractor laments:  we would like to remain an American company, but we need 70,000 engineers over the next ten years.  How can we accomplish both goals, when only one represents shareholder needs?  A senior education administrator meekly suggests the adoption of international standards, PISA, as a baseline for common standards only to be scolded by a peer; “But this is America!”

How do we untangle education?  By fighting over which fundamental levers matter, or turning to relevant data?

There is hope.

A student returns home from a day struggling to master Algebra as her teacher struggles to increase comprehension, while not ‘teaching to the test.’ The results of this test will drive the reputation of, and government investment in, the school district.  The reputation of the school district will drive housing prices, and shape neighborhoods. All are exhausted by the end of the school day, but the data collected at the point of learning remains tiny ovals filled in by a child’s number two pencil.

Returning home, the student unwinds by loading up a multi-player online immersive video game.  The players navigate a complex environment, their interactions driving the direction of the game, as the game algorithms respond to player progression through the landscape.  Each move is measured, assessed, and the game evolves along one of a thousand paths - this path of learning is determined by the player’s interactions, both with their computer environment and with one another.  The players are connected via voice connections, as they work as a team to navigate the game’s landscape - often matched up against a set of adversaries, a mirror-image team tackling the same challenges and competing with them.

The next morning, the student loads her textbook-laden rucksack and trudges off to sit in a classroom designed during the Victorian-era, hoping to color in the ovals correctly before Christmas.

Which experience better prepared her for the ability to think critically, understand variables, and make decisions amidst uncertainty?  What data matters in this story?

Let’s start here.

5 Minutes Regarding U.S. Education

I was privileged recently to present at an "IgniteDC" venue, an interesting format where you provide 20 slides for a five minute talk. The slides are automatically advanced every fifteen seconds. The results can be interesting, occasionally disjointed, and occasionally memorable. I don't know where mine landed, frankly, but it led to great conversations afterward - and I was doubly privileged to meet a few teachers in the audience who thanked me for the presentation. It is sobering to have heroes thank you just for talking about the challenges that define their careers. They are the ones who must be thanked.

Nevertheless, the state of U.S. education is somewhat dire. There are a few bright spots coming up this year: federal dollars tied to innovation and accountability; a new film that spotlights the needs of our most under-served children; graduate degree programs focused on leading a new education system, rather than navigating the current broken one; and more.

My hope was to begin the conversation, and trust we would have a more noble exchange of views than has characterized other public policy initiatives lately.

UPDATE 5/3/2010 - the video has been posted to Blip.tv and iTunes - embed below:

Learning in the Digital Age - John Seely Brown

Hat tip to Fred Zimny on finding this gem. I embed this video here because I wanted to also give some initial thoughts on what I've learned watching this.  You may be tempted to skip the video once you see it will take an hour out of your life.  This would be a mistake, but just in case I thought I would share some of my notes. At first, I hesitated when I saw the title "digital age," because I presumed I would be hearing more about the "digital learner," and how kids are just so different today.  I don't find there is much science to support this notion, and believe strongly that 'generational' characterizations are lazy, deny our shared humanity, and empower us to ignorance.  I'm looking at you, Myers-Briggs.

Much to my delight, John Seely Brown instead here touches on a core problem that I've had a hard time describing.  Specifically, and this comes during the Q&A: "there is no norm, no prototype, no typical example, in a power law distribution.  And the human mind is unprepared to reason about things that don't have examples."  We are trained to believe in Gaussian (normal) distributions, whereas much of our world is made up of power law distributions.

What? Brown gives an example:  what if architects had to account for humans who didn't adhere to a normal distribution for height, but rather a power law distribution?  There would be millions of us around 1 foot tall, and a few poor folks 1 thousand feet tall.  How would you design that building?  Fortunately for architects, humans generally follow a normal law distribution for height.  Unfortunately for the rest of us, much of the world does not.

Translation:  we are surrounded by 'black swans.'  The more we rely on the established wisdom about how the world works, the less prepared we are to succeed in a world that is in flux.  The good news is that our digital age, properly embraced, can help us adapt our notions of learning.  Our first inclination with new technology is to use it to evidence existing practices.  "Digitizing paper," if you will.  Moving beyond this will be key to embracing what Brown calls the "new disposition" for a digital age learner.

Other nuggets:

* The biggest obstacle to innovation is wisdom.

* Singapore is reinventing their education system based on a single maxim:  "teach less, learn more."

* Marking on a curve creates incentives that fight against social learning.  And all learning is social.

* Nothing clarifies ideas better than explaining them to others.

* Learning through creating, playing provides the foundations for constantly mastering a world in flux.  If your world is static, learn through teaching. If it is in flux, learn by tinkering.

Enjoy!

All Learning is Personalized

Nothing that is worth knowing can be taught. -- Oscar Wilde

Let’s imagine a conversation at the close of the 19th century.  You and a team of designers are considering elements of the internal combustion engine that will, if successful, trigger a revolution in personal transportation and change the course of history.  In a conversation with team members, you are presented with a series of challenging questions regarding the use of a sparkplug.Spark

“How do we know that’s the right design?  Where has this worked before?”

You are flummoxed because there is precious little evidence that you are on the right path.  You understand the principles of fuel and ignition, but you cannot demonstrate how the automobile will transform social structures and economies.  You are engaged in the new, and must resort to principles within known science rather than case studies.  You cannot predict how your creation will emerge and co-evolve in a new world, but in order to begin, you first establish some predictive rationale that lets you begin on a road that has the highest probability of success.

We who believe in systemic transformation for education are confronted with this challenge.  We cannot point to complete system exemplars, because the system we are encouraging does not yet exist. We instead develop principles of design that respect known science to the degree possible.

Let us take one of those principles, problematically titled “personalized learning.”  How do we know this is important?  Why the emphasis on learning, rather than instruction?  And why should the learning experience be tailored to the individual?    The first consideration when pondering how to help children learn should be to explore how they learn.  Fortunately, advances in neuroscience help us reconsider our approach to young minds, and answer some fundamental questions:  Are we born with a vessel into which knowledge is poured?  Or do we create our own mind?

Reviewing the science, we find that all learning is personalized.  Neuroscience, cognitive science, sociology, psychology, and philosophy agree - we create representations of our world based on individual experience.  No amount of instructional method can ensure an “accurate” uptake of information.  This is because you are designed to predict events in a complex world.  You do this by developing a consistent sense of the world around you, the memory of input patterns experienced from birth.  The infant brain is incredibly plastic, meaning it can change and rewire itself based on the type of inputs flowing into it.

When patterns appear familiar, you recall previous similar patterns and form a sense of the future based on them.  An intelligent human develops the ability to predict events in their environment, so that they may adapt themselves or elements of that environment to suit their interests and goals.

“The cortex is still dividing itself into task-specific functional areas long into childhood, based purely on experience.  The human brain has an incredible capacity to learn and adapt to thousands of environments that didn’t exist until recently.  This argues for an extremely flexible system, not one with a thousand solutions for thousand problems.” (Hawkins, p.54)

As the world is not a predictable machine, this means we do not develop complicated decision trees and Spock-like logic methods.  Instead, we explore, experiment, fail and learn about our world in physical and temporal context.  These learnings are shaped by individual experience, and are inherently intimate.  Our brains constantly create new structures with every new experience or piece of information - these structures are more specific to our individual humanity than our fingerprints or iris patterns.

You are designed to work with incomplete information.  The way you understand your world is through a combination of real inputs and memory.  You resolve ambiguity by continually filling in logical gaps based on learned patterns over time.  In conversation, not every word you hear is understandable out of context, rather, you predict the meaning of phonemes you hear based on the conversation itself.  This same principle applies when reading handwritten words - by themselves perhaps ambiguous, we resolve this by interpreting the context and resolving the meaning based on learned patterns. How does this work?

“Memories are stored in a form that captures the essence of relationships, not the details of the moment.  When you see, feel, or hear something, the cortex takes the detailed, highly specific input and converts it to an invariant form.  It is the invariant form that is stored in memory, and it is the invariant form of each new input pattern that it gets compared to.  Memory storage, memory recall, and memory recognition occur at the level of invariant forms.” (Hawkins, p.82)Cute Baby Boy Isolated on White

You resolve ambiguous input data based on how you believe the world works. This is due to our memory structures, which provide for “invariant form memory,” a memory of input patterns allow for partial patterns to recall whole ones.  This is what occurs when you see a friend in the mall - catching just a glimpse is enough for you to ‘recognize’ her.  This is termed ‘invariance.’  If you see someone at a bus stop partially obscured by a sign, you ‘assume’ the rest of her based on previous patterns that assume whole humans.  This ‘filling in’ of details occurs at the most detailed sensory input, where the blind spot we all have near the center of our eye is accommodated by previous cognitive patterns.  At the top of the cognitive hierarchy, where higher order pattern matching occurs, you experience the same ‘filling in’ for missing details.

This is true from the simplest form - we don’t notice the blind spot in every human eye, but rather complete the image based on surrounding context - to the most complex, including how we make decisions.  One author, discussing the reality of intuitive or ‘recognitial’ decision-making, notes: “The basic aspect of recognitional decision making is that people with experience can size up the situation and judge it as familiar or typical.  Usually this assessment happens so quickly and automatically that we are not aware of it.” (Klein, p.89)

As a student is not passively absorbing what is provided, but rather continuously storing patterns and comparing them against a unique collection of invariant form memories - we see the student is already in control of the learning experience.  This is not new age fluffy thinking, this reflects the reality that embedded experience frames and shapes how we understand our world.

Preparing children to succeed involves acknowledging each child’s centrality to the learning experience.  We can choose to continue methods that are convenient to the adult, mass lectures or student ‘tracking,’ or we can provide a system that adapts to the individual minds in our care at every stage.  The science leaves us no option here - ‘personalized learning,’ by whatever name, is a central design principle for a transformed education system.

Sources

Deacon, T. W. (1997). The Symbolic Species: The Co-Evolution of Language and the Brain. New York, NY: W.W. Norton & Company.

Goffman, E. (1974). Frame Analysis:  An Essay on the Organization of Experience. Boston, MA: Northeastern University Press.

Hawkins, J., & Blakeslee, S. (2004). On Intelligence. New York, NY: Henry Holt and Company, LLC.

Klein, G. (1998). Sources of Power: How People Make Decisions. London, UK: The MIT Press.