The education revolution at our doorstep

University education is at the brink of radical transformation. The revolution is already happening and the Khan Academy, Udacity, Coursera and the Marginal Revolution University are just the harbingers of a change that will soon sweep over universities throughout the world.

Alex Tabarrok has a must-read piece on the coming revolution in education here. The entire piece is highly recommended, so I am not gonna even try to summarize it here, but this part stands out:

Teaching today is like a stage play. A play can be seen by at most a few hundred people at a single sitting and it takes as much labor to produce the 100th viewing as it does to produce the first. As a result, plays are expensive. Online education makes teaching more like a movie. Movies can be seen by millions and the cost per viewer declines with more viewers. Now consider quality. The average movie actor is a better actor than the average stage actor.

As a result, Tabarrok predicts that the market for teachers will became a winner-take-all market with very big payments at the top: the best teachers would be followed by millions and paid accordingly.

My prediction is that the revolution in education will also lead to greater specialization – maybe you can’t be the best  Development Economics teacher, but you can be the best teacher on XIXth Century Agricultural Development in South-East Denmark: economies of scale brought by online education can make such uber-specialization of teaching portfolios profitable (or, indeed necessary).

Surprisingly or not, it is American entrepreneurs and institutions who lead this revolution. In Europe, online education is still relegated to pre-master programs and the like and is too often a thoughtless extrapolation of traditional education practices online. Sooner rather than later, the revolution will be at our doorstep. We better start preparing.

[P.S. the Guardian aslo run a recent piece on the topic as well]

How (not) to give an academic talk?

Some great advice by Cosma Shalizi. These are just the footnotes:

* Some branches of the humanities and the social sciences have the horrible custom of reading an academic paper out loud, apparently on the theory that this way none of the details get glossed over. The only useful advice which can be given about this is “Don’t!”… 

** … big tables of numbers (e.g., regression coefficients) are pointless; and here “big” means “larger than 2×2”.

The entire post is highly recommended.

Unit of analysis vs. Unit of observation

Having graded another batch of 40 student research proposals, the distinction between ‘unit of analysis’ and ‘unit of observation’ proves to be, yet again, one of the trickiest for the students to master.

After several years of experience, I think I have a good grasp of the difference between the two, but it obviously remains a challenge to explain it to students. King, Keohane and Verba (1994) [KKV] introduce the difference in the context of descriptive inference where it serves the argument that what often goes under the heading of a ‘case study’ often actually has many observations (p.52, see also 116-117). But, admittedly the book is somewhat unclear about the distinction and unambiguous definitions are not provided.

In my understanding, the unit of analysis (a case) is at the level at which you pitch the conclusions. The unit of observation is at the level at which you collect the data. So, the unit of observation and the unit of analysis can be the same but they need not be. In the context of quantitative research, units of observation could be students and units of analysis classes, if classes are compared. Or students can be both the units of observation and analysis if students are compared. Or students can be the units of analyses and grades the unit of observations if several observations (grades) are available per student. So it all depends on the design. Simply put, the unit of observation is the row in the data table but the unit of analysis can be at a higher level of aggregation.

In the context of qualitative research, it is more difficult to draw the difference between the two, also because the difference between analysis and observation is in general less clear-cut. In some sense, the same unit (case) traced over time provides distinct observations but I am not sure to what extent these snap-shots would be regarded as distinct ‘observations’ by qualitative researchers. 

But more importantly, I start to feel that the distinction between units of analysis and units of observation creates more confusion rather than more clarity. For the purposes of research design instruction, we would be better off if the term ‘case’ did not exist at all so we could simply speak about observations (single observation vs. single case study, observation selection vs. case selection, etc.) Of course, language policing never works so we seem to be stuck in an unfortunate but unavoidable ambiguity.

Overview of the process and design of public administration research in Prezi

Here is the result of my attempt to use Prezi during the last presentation for the class on Research Design in Public Administration. I tried to use Prezi’s functionality to provide in a novel form the same main lessons I have been emphasizing during the six weeks (yes, it is a short course). Some of the staff is obviously an over-simplification but the purpose is to focus on the big picture and draw the various threads of the course together.

Prezi seems fun but I have two small complaints: (1) the handheld device I use to change powerpoint slides from a distance doesn’t work with Prezi, and (2) I can’t find a way to make staff (dis)appear ala PowerPoint without zooming in and out .

What makes a video go viral?

Internet Marketing expert Dr Brent Coker claims to have developed an algorithm that can predict which ad movies will go viral on YouTube. I don’t plan a career move to advertising but was nevertheless intrigued by the claim from a research methods & design perspective. Unfortunately, there is very little information available (yet?) and what information is available makes me a bit skeptical about the reliability of the conclusion. Still, Dr Coker’s approach might make for a nice discussion in the context of a Research Design course since it touches upon a question students can relate to, and raises various issues from operationalization to theory specification to theory testing.

In short, according to Dr Coker, “there are four elements that need to be in place for a branded movie to become viral: (1) congruency, (2) emotive strength, (3) network-involvement ratio, and (4) paired meme synergy”. Congruency is the consistency of the video’s theme with brand knowledge. Disgust and fear, for example, imply powerful emotive strength. The network-involvement ratio refers to how relevant the message is to the seeded network. The last element ‘paired meme synergy’ means that certain memes are effective when paired with certain other memes. “For example, impromptu entertainment acts appeared to work when paired with ‘Eyes Surprise’. When paired with ‘bubblegum nostalgia’, the … pair doesn’t work. Anticipation works with Voyeur, but not on its own. And so forth.”

As I said, there is not much information available on the research design, but from what I can gather, the predictive algorithm is based on an inductive approach: analyze movies that did go viral and see what their characteristics are. Such an approach would be OK to generate ideas, but one should be careful overselling the inductively-identified “solution” as a predictive algorithm which has been properly tested. An obvious next step would be to see whether the “solution” predicts outside the sample it was derived from, and maybe Dr Coker is working on that stage now. I wonder, however, whether the rather flexible definitions of some of the predictive elements make a testing of the approach feasible even in principle. It seem hard to identify the ‘network-involvement ration’, for example, prior to observing the outcome. The meme-pairing idea is interesting, but again: if there is no clear idea why certain memes should go together, there is a high risk of the analysis just playing catch-up with the data.

For example, how would you score this awesome recent viral video (my take would be Disruption Destruction + Performance + Skill Bill + Simulation Trigger, for the list of possible memes see here)?: 

P.S. On a somwhat related note: The Atlantic has a feature on the rise of big data which says that Google runs ”100-200 experiments on any given day, as they test new products and services, new algorithms and alternative designs”.