  Massive Open Online Courses (MOOCs) platforms are becoming increasingly
popular in recent years. Online learners need to watch the whole course video
on MOOC platforms to learn the underlying new knowledge, which is often tedious
and time-consuming due to the lack of a quick overview of the covered knowledge
and their structures. In this paper, we propose ConceptThread, a visual
analytics approach to effectively show the concepts and the relations among
them to facilitate effective online learning. Specifically, given that the
majority of MOOC videos contain slides, we first leverage video processing and
speech analysis techniques, including shot recognition, speech recognition and
topic modeling, to extract core knowledge concepts and construct the
hierarchical and temporal relations among them. Then, by using a metaphor of
thread, we present a novel visualization to intuitively display the concepts
based on video sequential flow, and enable learners to perform interactive
visual exploration of concepts. We conducted a quantitative study, two case
studies, and a user study to extensively evaluate ConceptThread. The results
demonstrate the effectiveness and usability of ConceptThread in providing
online learners with a quick understanding of the knowledge content of MOOC
videos.
