Machine Learning Predicts Students’ Final Grades As the Course Unfolds

“Can we predict a student’s final grade based on his or her behavior in the course so far?”

Writing for the Wall Street Journal, Don Clark showcases Canadian company Desire2Learn, a provider of cloud-based learning systems to enterprises and academia that recently announced this very capability.

With 10 million learners over 14 years, the company has collected detailed records on student engagement with instructional materials and their subsequent performance on tests.

Desire2Learn has developed machine learning algorithms it applies to its historical data that make predictions of how students will fare as the course unfolds.

Such predictive analysis serves as an early warning signal so instructors can give at-risk learners the additional, personalized attention they need, when they need it most.

The company’s CEO, John Baker, claims Desire2Learn’s algorithms yield greater than 90% accuracy at predicting letter grades.

John Baker, CEO Desire2Learn (c) Desire2Learn

John Baker, CEO Desire2Learn (c) Desire2Learn

Just the same, privacy issues do crop up. For example, instructors having student’s individual engagement statistics can expose a student’s general level of effort.

As a safeguard, Desire2Learn anonymizes personally identifiable information about student activities by stripping off such data when the student finishes the course.

The company makes predictive findings available to instructors through its Students Success System and they plan to do likewise for students through Degree Compass, a product currently in beta.

So, how would your business change if you had time predictions about future outcomes?

Comments are closed.