Predicting Education Success with Machine Learning

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Don Clark of the Wall Street Journal reports, “Computer-based educational systems have long helped impart information to students and assess their understanding of it. The next step, one company in the field says, is using their behavior to make predictions. That’s the aim of technology being announced Tuesday by Desire2Learn, a Canadian company that specializes in cloud-based based learning systems it markets to colleges, schools and companies. Desire2Learn, launched in 1999, competes with companies like Blackboard and Instructure. It claims that 10 million learners at a range of institutions have made use of its technology, including some at big U.S. university systems.”

Clark continues, “John Baker, the company’s president and CEO, says that over the years it has gathered extensive data about students’ usage of its software, including records on how often they read or otherwise engaged with instructional materials and how they subsequently performed on tests. With the aid of that data, Desire2Learn has developed a series of machine learning algorithms to make predictions about how users ultimately will perform based on their activity as online classes progress, Baker says. The idea is to give instructors’ early warning about students that might need extra encouragement or individualized attention.”

He adds, “Baker says there is a considerable amount of such ‘predictive analytics’ techniques used on sites like, which gives customers hints like showing that people who bought a particular product often bought another set of products. And there are various analytic engines in existing online learning software, some of which may alert teachers if a student is doing something obviously wrong, he says.”

Read more here.

Image: Courtesy Desire2Learn

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