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Education, Technology, and Empirical Data

I just returned from the Institute for Advanced Study’s Symposium on Technology and Education. Anyone interested in how education operates should contact the folks in today’s symposium or in the year-long seminar The Dewey Seminar: Education, Schools and the State. It is a great group of people thinking about justice, finance, the structure of schools, education and labor matters, whether constitutions address education, and much more. Indeed, it struck me that many of the participants’ work could provide interesting opportunities for collaboration.

Today’s speakers offered some fantastic ideas about the way education works in K-12. One thing that occurred to me was how, in yet another field, data is increasingly important. In many areas, vast amounts of data are being used to understand how a student is performing or where a different type of learning style may be required or whether a teacher is effective, and so on. This point may be readily familiar to those interested in empirical legal studies. Yet, two key issues arise. How does one sort the data? And, how does one interpret the data.

The answer seems to lie in the ability to embrace the Google mindset. Take in data. Study it. Play with it. Study it. Play with it. Study it. Play with it. And see where it takes you. As Hal Varian has described (pdf), “The real secret to Google’s success is that they are constantly experimenting with the algorithm, adjusting, tuning and tweaking virtually continuously.” He compares this approach to “the Japanese approach to quality control is kaizen which is commonly translated as ‘continuous improvement.'” As general matter Varian has offered:

During the 1960s and 70s the scientific study of financial markets flourished due to the availability of massive amounts of data and the application of quantitative methods. I think that marketing is at the same position finance was in the early 1960s. Large amounts of computer readable data on marketing performance are just now becoming available via search engines, supermarket scanners, and other sorts of information technology. Such data provides the raw material for scientific studies of consumer behavior and I expect that there will much progress in this area in the coming decade.

After today’s seminar I am wondering whether “large amounts of computer readable data on marketing performance” could also be written “large amounts of computer readable data on education performance.” It seems like that day is coming, if not already here. We may be entering an era where education is heavily data driven and educators must be able to use new tools to understand and use the data. The challenges regarding privacy, notions of tracking, and fairness will be large. Then again the promise of improved educational outcomes and a system that can reach more students in ways far beyond training them to jump through test-taking hoops suggests that whatever the obstacles, it is worth pursuing the possibilities.