Automated Segmentation of MOOC Lectures towards Customized Learning

Abstract

The sheer size of the student body for MOOC and the diversity of their learning styles and backgrounds demand that we develop alternatives to the one-size-fits-all pedagogy used in residential education. An important aspect of this endeavor is the segmentation of the video material, since it forms the omnipresent and central part of every course, and structuralized videos allow non-linear navigation as well as help learners with various needs find desired information efficiently. Here, we propose an automatic visual transition detection method to partition lecture videos into self-contained segments, which is the foundation to structuralize video and support non-linear navigation. Our method can be done at scale and has been proved being able to achieve reasonable quality.