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Automatic Knowledge Discovery in Lecturing Videos via Deep Representation | IEEE Journals & Magazine | IEEE Xplore

Automatic Knowledge Discovery in Lecturing Videos via Deep Representation

Publisher: IEEE

The pipeline of automatic identification of knowledge in lecture video.

Abstract:

In recent years, e-learning systems such as massive open online courses (MOOC) have been widely employed in academic institutions and have shown its power in enhancing th...View more

Abstract:

In recent years, e-learning systems such as massive open online courses (MOOC) have been widely employed in academic institutions and have shown its power in enhancing the students’ learning ability. Automatic detection and classification of knowledge points in lecture video would significantly enhance the performance of the online learning platform. Most of the previously presented approach for knowledge discovery focused on the text and audio documents, whereas the identification of knowledge points in videos still remains a challenge. To bridge this gap, we proposed a novel convolutional neural network which was designed for the characteristics of lecture video. It could both extract the temporal–spatial and semantic information from the multimedia record. To evaluate the performance of the proposed technique, we conducted comparison experiments between the state-of-the-art methods and ours. The experimental results demonstrated that the presented approach outperformed the state-of-the-art techniques and could be potentially invaluable for the accurate discovery of knowledge points within videos.
The pipeline of automatic identification of knowledge in lecture video.
Published in: IEEE Access ( Volume: 7)
Page(s): 33957 - 33963
Date of Publication: 11 March 2019
Electronic ISSN: 2169-3536
Publisher: IEEE

Funding Agency:


References

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