The role of learning analytics in the future of education

Ellen Wagner and Phil Ice’s article, “Data Changes Everything: Delivering on the Promise of Learning Analytics in Higher Education,” does a nice job of discussing the positives and negatives of educational data analytics.  That field will be one of the most dynamic areas in education over the next several years.  We are really in the infancy of what is possible with learning analytics and the growing abundance of data to be analyzed.  I was a big fan of the Moneyball approach in baseball. The Oakland A’s used data analysis to identify high achievers who were undervalued.  It has allowed them to have great success with lower budgets than the vast majority of clubs.  The A’s have, more often than not, been in first or second place since 1999, while most of the time their budget has been well into the lower third of team spending.  I can appreciate the model of approaching the formula for winning from a different angle – that is, doing things differently than conventional wisdom dictates.

How well that type of Moneyball success can translate to education isn’t as clear. No one is exactly sure how to best use the variety and volume of data yet, though the goals are there.  The better, cheaper, faster approach is always attractive.  For example, colleges could use learning management system data to help students succeed through early detection of struggling students – perhaps before the students themselves are even aware of their plight.  That student centered goal supports an institutional goal of increasing retention and can, perhaps, even be accomplished more cheaply than we currently do so with the increasing availability of learning data.  However, the data collection and analysis could lead to unintended consequence of being a slave to results or, alternatively, suffering from analysis paralysis in not being willing to act without one more study.  While there are lots of directions that the future could hold, I think it clear that there will be good opportunities for DE professionals with data analysis skills who can practically understand what the data is or is not telling us and who can communicate the results simply.


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