The Troubled Future of Higher Education

The political scientists Gary King and Maya Sen have just posted an excellent working paper clearly outlining the major problems facing higher education: economic, political, sociological. The main thrust is that, although only 30% of the American population obtains a four-year college degree (thus leaving an untapped 70% who could finish college degrees), the higher education system is facing major constraints due to limited budgets and major technological advances. For example, online sites such as Khan Academy are effectively competing with universities, and for-profit universities are growing at a high rate. I'd add to their list the potential for big data analysis to displace the role of experts; I refer to the effect of sabermetrics on baseball journalists or data mining algorithms on marketers as possible canaries in the cage for academics. Regardless, King and Sen's paper is a must-needed beginning of a discussion about the future of higher education in the wake of profound social changes. After all, it was only a mere decade ago that Time and Newsweek were major cultural institutions in American life.

90+ Two-Minute Videos on R

I highly recommend Anthony Damico's excellent two-minute videos on programming in R. You can find the full list of 90+ videos here. This is the first of the series, which tells you how to download and install R:

More generally, Anthony's video collection is another reminder of the immense sociological benefits that come from sharing educational materials and expert knowledge in the style of the Khan Academy.

Global Online Conference on Statistics

The Consortium for the Advancement of Undergraduate Statistics Education is hosting a global online conference titled "eCOTS: Electronic Conference on Teaching Statistics." You can view the full program here. It only costs $15 to register and participate in the online conference. For at least the past five years I've thought that conferences are obsolete in many respects, so I'm delighted to see this conference developed. By not having a physical place, with food, beverages, and equipment, not to mention lodging and transportation costs, the costs of attendance are much lower, thus enabling more and more people to learn and contribute to knowledge production. (Of course, we'll still want some conferences for face-to-face socialization!)

The Future of the Academy in 2032

Just before he died, for a few years I helped the great sociologist Dan Bell with using his computer, and as a result I got to know him very well. One thing I learned from him (besides the distinction between "criticism" and "critique") is the usefulness of prediction as an endeavor in itself (as opposed to explanation). In this spirit, I offer five predictions about the future of the academy in 2032:

1. First, despite opposition from many established institutions, there will be an enormous increase in open-source education. Classes on any topic will be available online for free, with lecture notes, videos, presentations, and chat services (with other students) available to anyone with a computer. Exemplars of this trend include MIT OpenCourseWare, Khan Academy, and videolectures.net. 
2. Second, academic publishing will be increasingly online, with peer review a continuous process. Rather than books and articles published at one time in paper form after a process of peer review, academic projects will be ongoing, process-oriented, available online, and subjected to a continual process of peer review. In essence, everything that academics produce will be works-in-progress, and updated when errors are noted. Early indications of this trend include the NBER archive and  arxiv.org.
3. Third,due to technological changes and increased monitoring of people's activity, academics will have to be adept with managing and analyzing big data. Common statistical methods will often be difficult to use on such large data sets, straining the computational capacities of computers. While not common in the academy yet, big data is one of the top buzzwords of 2012, and I expect this to spread to academic work relatively soon. An exemplar of this kind of academic work is the Google ngrams project. (One danger, however, is that private corporations might be hostile to information-sharing, and the values of profit-making may severely inhibit the availability of big data to academics.)
4. Fourth, big ideas will actually be in greater demand in the future. Precisely because there will increasingly be an excess of information, grand theories and master narratives will be increasingly desired to help guide attention, avoid fragmentation of different research traditions, and unify otherwise disparate theories. For example, Josh Tenenbaum's efforts at unifying artificial intelligence (which suffers from disciplinary fragmentation) with probabilistic graphical models is a promising endeavor.
5. Finally, the skills in demand will be increasingly modular rather than topical. For example, as part of the Cold War in the 1960s, the United States government funded various "area studies" programs to educate Americans on the traditions, customs, and practices of various geographic regions around the world. In the future, there will be less emphasis on this kind of topical knowledge, and greater emphasis on modular skills  such as critical analysis of any kind of texts or arguments, understanding the basic structures of any set of languages, and gathering and analyzing various kinds of qualitative and quantitative data. 

To the extent any of these predictions are correct, sociology is particularly well-suited to take advantage of these trends. Sociologists are generally supportive of the democratic, inclusive principles of open-source education and online publishing, and sociology has an unparalleled tradition of big ideas. Moreover, modularity is ingrained in the discipline; in fact, sociology is almost by definition a modular discipline, inasmuch sociology is an approach to a particular subject matter rather a particular subject matter per se.

Misc. Lectures Online

I highly recommend the following lectures for anyone interested in social science research using quantitative methods:

• The late Sam Roweis (a brilliant educator who died unexpectedly several years ago) gives a superb introduction to machine learning and probabilistic graphical models here, complete with lecture slides. In case you aren't aware, probabilistic graphical models are in effect a unifying approach to a wide range of statistical models, from hidden Markov models to hierarchical Bayesian models.
• Salman Khan, the MIT graduate who started the eponymous Khan Academy, offers a superb series of lectures on probability, available here. Probability is actually the foundation for quantitative research in the social sciences, since much of the goal of inference is to quantify uncertainty through the use of probability distributions such as the Gaussian, Poisson, Gamma, and so forth.
• Although for programmers in python, the computer scientist Allen Downey gives a thorough, intuitive, and entertaining overview of Bayesian analysis, which you can view in its entirety here.

Values and Politics

I'm a bit biased, but the front page of the Huffington Post highlighted a fascinating study on education, culture and politics today.