Automated Generation of Learning Paths at Scale

Abstract

Content creation has long been regarded as one of the most challenging obstacles to personalized learning. In recent years, however, online platforms have managed to mobilize both audiences and content creators in large numbers, creating new opportunities to revisit the pursuit of personalization at scale. We describe initial results from a real-world implementation of a system for algorithmically generating learning paths at Udemy.com, a two-sided online educational marketplace with over 150,000 courses and over 50 million users. Our initial investigations suggest the potential effectiveness of automated approaches for guiding self-directed learners toward courses that help them achieve their desired learning outcomes.