ConceptThread: Visualizing Threaded Concepts in MOOC Videos

TL;DR

ConceptThread is a visual analytics tool that extracts and visualizes concepts and their relations from MOOC videos, helping learners quickly grasp course content through interactive exploration.

Contribution

This paper introduces ConceptThread, a novel visualization approach combining video processing and speech analysis to display hierarchical and temporal concept relations in MOOCs.

Findings

Effective in providing quick understanding of MOOC content

User studies show high usability and engagement

Facilitates interactive exploration of video concepts

Abstract

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.