Learnersourcing : improving learning with collective learner activity

Abstract

Millions of learners today are watching videos on online platforms, such as Khan Academy, YouTube, Coursera, and edX, to take courses and master new skills. But existing video interfaces are not designed to support learning, with limited interactivity and lack of information about learners' engagement and content. Making these improvements requires deep semantic information about video that even state-of-the-art AI techniques cannot fully extract. I take a data-driven approach to address this challenge, using large-scale learning interaction data to dynamically improve video content and interfaces. Specifically, this thesis introduces learnersourcing, a form of crowdsourcing in which learners collectively contribute novel content for future learners while engaging in a meaningful learning experience themselves. I present learnersourcing applications designed for massive open online course videos and how-to tutorial videos, where learners' collective activities 1) highlight points of confusion or importance in a video, 2) extract a solution structure from a tutorial, and 3) improve the navigation experience for future learners. This thesis demonstrates how learnersourcing can enable more interactive, collaborative, and data-driven learning.