The Evolution of Higher Education

Those who don't work in higher education -- and, unfortunately, many who do -- will not have heard of MOOCs (pronounced "mook").  It's a funny name for one of the biggest threats to academia in recent times.  MOOC stands for Massive Open Online Course, often reaching tens of thousands at a time, and usually constructed by deep-pocketed high-powered institutions like Harvard and Stanford.  And here's the worst part: they're free.

Think of what happened to the music industry in the last ten years, or to publishing, or to newspapers and the phone company.   This is what is coming to colleges and universities everywhere, except for those like Harvard, Yale, Stanford.  They have brand recognition, an elite clientele, and anyway they are the ones who are writing the MOOCs.  It's little institutions like mine, lesser known and with just 11,000 students, which must take notice.

There may be a silver lining to the dark cloud, however.  Higher education might actually benefit from a little shaking up.  The academy is, to some extent, locked to some older models of learning which don't work as well today.  For example, departments have what is called a "silo mentality" and often deliver their disciplines as if they were not connected to one another.  In adjacent offices there may be a biologist and a psychologist, or a sociologist and an economist, or a physicist and a graphic artist.  And they don't speak.  A few disciplines are different, at least in theory.  Historians must address major events as they come along through time -- some are sociological, some political, economic, meterological, and so on.  A geographer looks at all manner of things too, in the context of location and place.  But most disciplines (and even these, sometimes) see themselves as so unique they are almost like different species. 

But actually the opposite is true.  The biological definition for species is groups which don't produce fertile offspring in a natural setting.  Some species could have productive offspring but they don't mate for behavioral reasons -- domestic dogs and coyotes, for example.  But the real world is exactly where the disciplines do cross-pollinate.  Nature is interdisciplinary.  Silos -- the species which don't have fertile offspring -- only appear in captivity.  That is, in the academy. 

Another problem in the academy is the polarization -- often antagonism --- between faculty and administration.  I've written about this earlier in Reframing Organizations but it basically comes from highly trained specialists being told what to do by a structured organizationists.  The frustrated herder may come to hate the cats, and the cats to hate the herder.  Neither side understands the challenges and pressures the other one faces.  But while this is going on, MOOCs present a common enemy.  There's nothing like an attack by aliens to make nations get along.

And it's also true that competition can be a force for change.  It's an evolutionary fact: survival of the fittest.  It's a process of variation (technology borne MOOCs) ... and elimination.  I only hope it's elimination of less effective ways of teaching, and not of the institution itself.     

I'm going to describe my experience with MOOCs in detail in my next blog, as I'm just midway through two of them now.  I'm taking Genetics and Evolution from a professor at Duke University, and Game Theory from three at Stanford.  I'd say they're giving me a run for the money, a nice challenge,  but I can't because there is no money involved.  I'll share more later but I'll just say that more than 20,000 are enrolled in one, and 15,000 in the other, and the discussion boards are full of highly educated chatter.

So what is the appropriate response to MOOCs in higher education?  There are several interesting trends I'm aware of, one of which is called the "inverted curriculum."   Normally, a course is designed to provide students with the building blocks first -- simple concepts, background, history, definitions, and so on, then the more complicated ideas to culminate if they're lucky, in an application of the new knowledge to a real situation. With an inverted curriculum the opposite happens -- you start with a "nasty," problem, for example, the threat of  Asian Carp to the Great Lakes.   Study the problem first, look at its various dimensions, understand its implications, and basically presente students with messy problems like those in the real world.  Then dig down into the discipline -- or disciplines -- to understand the issues better or move toward a solution.

At a recent conference that discussed this approach (SENCER) I saw this in action. A group of us were told about the Rhesus monkeys which had been attacking people in New Delhi. We saw a short video of monkey thugs terrorizing park-goers, and watched a news report describe people being thrown from the roofs, chased, bitten, robbed, and attacked in alleys.  Monkeys break into homes to raid refrigerators.  At the same time, an important Hindu sect worships the monkey god Hanuman, and actually feed and protect the animals.  

source: http://www.mererhetoric.com/images/monkeysindia1.jpg

We were all impressed by the challenge and broke briefly into small groups. Then the chemist talked a bit about discrete sterilization, maybe with darts. A psychologist had studied Rhesus behavior and suggested ways they might be conditioned.   I and another geographer thought of tracking their travel patterns with small GPS devices, mapping incidents, and comparing these to the distribution of Hanuman temples. A biologist mentioned disease vectors and growth rates, and it went on this way around the room.  There are so many ways to sink one's teeth in the problem, so to speak, and it was clear to me that this approach would appeal to students.

Hey so here's the nasty problem of the day: MOOCs.

But it's complicated; there is a lot to recommend them, just as some worshipers and naturalists certainly appreciate the monkeys.  After all, higher education is sinfully expensive today, and also ... MOOCs are not at all bad for learning!  So another trend within higher education -- one that takes direct advantage advantage of the new technologies -- is  the "flipped" classroom.  This approach doesn't lose sight of the value of face time, but it makes use of it differently.  In one way it's like a hybrid course -- half online, and half in house.  But in the flipped classroom lecture is listened to outside of class, with podcasts.  And what was homework is moved to class time.  That is, students go to class for discussion, peer work, debate, projects, studying, application, field trips, experiments, and to give each other feedback on writing, and so on -- the kinds of things they used to do on their own time.  Although the approach is new and the science is young, I've read that from a learning perspective this is best -- better than classroom lecture, better than "traditional" online, better than hybrid, and -- thankfully -- better than a MOOC.

A third trend capitalizes on something MOOCs simply can not do, at all.  That is to increase civic engagement and community outreach.  This meshes well with the "nasty problem" approach, as many community organizations wrestle with just that sort of thing.  Students learn well by doing, and the communication skills they will acquire in a real setting are great for personal growth and employment prospects.  This is similar to what is often done by internship, but with a whole classroom at once, which allows for peer interaction.  And they give back to the community, at the same time.  Everybody wins.

So it seems that there may be hope for the regular universities, even in the face of Massive Open Online Courses.  But only if we are serious about it, and if we are quick.  OK then; we'll know, soon enough.   

The Signal and the Noise (a review)

Nate Silver's famously accurate political forecasts on his blog FiveThirtyEight gave me a little emotional stability going into the 2012 presidential elections. He hits almost every election right, and I liked how his predictions didn't vacillate with every new event.  And my favorite candidate had a 91% chance, the morning of.  So I read his book.  

It turns out his method is simple.  Silver, who now works for the New York Times, averages many political polls after weighting each on the basis of the polsters' past successes, the sample size, and when the poll was taken. He delivers the result in terms of probabilities, which allow for things unknown and imperfections in the model. He's smart, and startlingly accurate -- so when I heard he had written a book on probability I ordered it immediately. I ordered myself three copies by accident, such was my enthusiasm.

Bayesian Probability – the heart of the book -- is a simple statistical technique that corrects for shortcuts in our thinking. It was one of the lessons in Kahneman's extraordinary Thinking Fast and Slow. Bayes factors in the prior expectations in addition to several estimated probabilities for events occurring and not occurring. The method requires constant revision as new data come forward. I like it so much I've installed a Bayes app for my smart phone.

Silver is a serious gambler, we learn, and gamblers must be especially honest in their forecasts. They put their money where their mouth is.  Silver’s own games have been mostly poker and baseball and he devotes a full chapter to each of these. You wouldn’t know it from the cute titles (such as “How to Drown in Three Feet of Water”), but each chapter is sharply focused on a particular subject: 1. mortgage collapse, 2. politics, 3. baseball, 4. weather, 5. earthquakes, 6. GDP, 7. epidemics, 8., Bayesian probability itself, 9. chess, 10. poker, 11. stocks, 12. climate change, and 13. terrorism. Each of these, it turns out, is quite an interesting challenge as they vary in their complexity, the amount of data which are available, forces for bias, and the quality of the models used in forecasting.

I've drawn several sweeping conclusions. Good forecasting requires a lot of reliable data, a clear understanding of the baseline, and while a causal model is not essential it will help sort the signal from the noise. The forecast will benefit from multiple viewpoints (though not weighted equally), and from both quantitative and qualitative methods. It must be constantly reassessed as new data come forward. Computers are excellent for processing information, but if you put enough data into them they will detect patterns and relationships that don't really exist. Usually human judgment should figure in -- in part, and interestingly, because people have good vision.

People are irrational in predictable ways. Recent, local, and highly publicized events appear more important than they actually are. We tend to select information to support prior biases –especially when there is a lot of information available (and so this is more of a problem, in the informaton age). We easily attribute actual mistakes to bad luck. We are overconfident of our opinions and wedded to them. We can easily discount the possibility of extreme events.  And we are inclined to detect meaningful patterns where there are none. The author speculates, as would I, that there are evolutionary reasons for many of these.

There are some pretty impressive successes in forecasting, though. Chess is now a computer's game because all the usefull skills and combination of profitable moves can be programmed. Poker is fairly easily forecasted, because it’s relatively easy to calculate probabilities, and the human element is fairly small. Silver played himself for many years, ending only when on-line gaming rules drove out the poorer players -- the "fish," he calls them; one day he realized he was one.

Baseball also has a fixed rulebook and scads of data, but with a more complex human element which is more difficult to predict.  In these cases, Silver says, a wider net for information is better – interviews, profiles .. in other words, an old-fashioned talent scout.  More impressive still are the weather forecasts, now quite accurate eight days in advance. These also incorporate a great deal of data, and they benefit from the immutable laws of physics, and from elaborate models which have been refined and corrected by a steady stream of feedback data. Interestingly, commercial services offer 15 day forecasts even though anything beyond eight is less accurate than the baseline (climatological data alone, which is no forecast at all).   Apparently people sometimes prefer to feel informed, even with misinformation. The commercial forecasts, which are based on the government's models, are also jiggered.  The Weather Channel rarely predicts a 50% chance of rain, because their viewers are more comfortable with 60% or 40% -- and the chance of precipitation is routinely overstated (people enjoy unexpected sunshine, and they really hate bad surprises!).   Newscasters and political pundits are terrible forecasters -- they are often biased (towards sensationalism, or a particular party) and there is no cost to them, of being wrong.

If weather has become easy to forecast, climate is not. There is far less corrective feedback data, as climate changes so slowly, and so the models are inferior.  They're also more complicated as they must incorporate the greenhouse effect, and quite a lot more chemistry.  Add to this the strong political bias, because the causes, effects, and costs fall differently around the world. Many players are biased by self interest, others by pure contrariness, and because the science is incomplete some simply get confused and deny climate change altogether. However, there is wide agreement that C02 and other greenhouse gasses increase warming, human activities are increasing these gasses, and the world is slowly warming (~1.5dC/century), at least partly as a result of human activity. These details are debated: how fast it will happen, how temperatures and precipitation will change in specific regions, economic impacts, and how effective we might be slowing or reversing it.  But if you every hear someone claim that there is no evidence that the global climate is changing, that scientists are merely promoting a political agenda, or that we don't know enough about climate to predict anything with confidence ... that is not signal. That's noise.

There are things even more difficult to predict than climate: Earthquakes, largely because the most relevant data are deeply buried and hard to gather. And terrorism – for much the same reasons. Thankfully (I suppose) both of these seem to have a power law distribution such that when plotting frequency and magnitude on two logarithmic axes, events form a straight line. This is useful predicting even the frequency of events larger than any before.   So we know how likely, but we don't know when.  Or where.

That’s enough review – I've skipped a lot of my highlights and I highly recommend reading the book through. At times Silver gets drawn into irrelevant details such as building up a story which only ends in being summarily denied an interview.   He likes baseball a whole lot more than I do.  But mostly the book is rock-hard practical.

Other quibbles -- the word data is plural; a datum is, but the data are. And there were a few typos (though when I find one in a book of this quality it's like uncovering a nice little fossil).   It's not short – 454 pages, followed by 80 pages of notes and references – but I wanted more.  There should be chapters on the job market, on real estate, marriage, religious predictions, the efficacy of legislation,such as gun control, marijuana legalization, etc.  These are even more important than baseball, or poker.  I do think we need another book.

Mr. Silver?