Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

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.

Keywords

Computer aided instruction, Concept map, Data mining, Data visualization, Education, Electronic learning, MOOC summarization, online learning, Videos, Visual analytics, visualization in education

Discipline

Online and Distance Education | Software Engineering

Research Areas

Intelligent Systems and Optimization

Publication

IEEE Transactions on Visualization and Computer Graphics

First Page

1

Last Page

17

ISSN

1077-2626

Identifier

10.1109/TVCG.2024.3361001

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Additional URL

https://doi.org/10.1109/TVCG.2024.3361001

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