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The “Netflix for education” analogy has become somewhat of a cliché for edtech companies using student data to recommend anything from courses to textbooks. The pitch is simple: Why waste time choosing, or leave it to chance of whether a human advisor will understand your unique situation, when an algorithm can tell what you want based on your academic history?
That idea relies on a technology known as predictive analytics, a statistical model that analyzes past data to make estimations about some future event or trend. In higher ed, that data often includes student grades, test scores, attendance and, in some cases, even demographics.
Located about 50 miles north of Nashville, Austin Peay State University in Tennessee has a downhome and cozy feel, sprawling with red brick buildings and some offices that look more like cottages. Yet the campus has gained a techie reputation as one of the birthplaces for predictive analytics in education through a tool dubbed Degree Compass. Officials at the college, which has just over 10,000 undergraduate students, built the platform to recommend courses based on how well students did in previous ones.
At first, Degree Compass was met with fanfare and praise — and data to back it up. Overall student graduation rates rose from 31 percent in 2010 to 36 percent in 2014, for example. But today, the tool’s hype has died down, and campus officials and students say it hasn’t lived up to all of its promise. Graduation rates haven’t budged much since 2014, and retention recently dropped to below the point it was when the tool was introduced.
Now, a college that helped pioneer predictive analytics is finding the technology’s limitations.
Read the full article about how a "Netflix" model for education lost its luster by Sydney Johnson at EdSurge.