Making Static Lessons Adaptive through Crowdsourcing & Machine Learning

Text components of digital lessons and problems are often static: they are written once and too often not improved over time. This is true for both large text components like webpages and documents as well as the small components that form the building blocks of courses: explanations, hints, examples, discussion questions/answers, emails, study tips, motivational messages. This represents a missed opportunity, since it should be technologically straightforward to enhance learning by improving text, as instructors get new ideas and data is collected about what helps learning. We describe how instructors can use recent work (Williams, Kim, Rafferty, Maldonado, Gajos, Lasecki, & Heffernan, 2016a) to make text components into adaptive resources that semi-automatically improve over time, by combining crowdsourcing methods from human computer interaction (HCI) with algorithms from statistical machine learning that use data for optimization.