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IJIET 2013 Vol.3(3): 304-309 ISSN: 2010-3689
DOI: 10.7763/IJIET.2013.V3.286

Constructing Knowledge Representation from Lecture Videos through Multimodal Analysis

Pak-Ming Fan and Ting-Chuen Pong

Abstract—E-learning has presented new opportunities for learning with the rapid development of information and communication technologies (ICTs). Learners are no longer restricted by the location and time to learn. Lecture video is one of the most commonly used learning materials on e-learning platforms. It presents knowledge in a lively manner and keeps the learners more attentive during the learning process. While organizing lecture videos in a sequential list seems to be a natural choice, it presents the problems of inefficiency in searching for domain concepts and the inability to show relationships between such concepts. In this work, the task of constructing a knowledge representation scheme for video corpuses is explored. The knowledge representation aims to achieve the goals of facilitating the searching of domain concepts and to extract the relationships between the concepts so as to identify effective learning strategies for the corpus. A framework using text recognition, speech recognition, multimodal analysis and clustering techniques is proposed for the construction of the knowledge representation. Two lecture video corpuses on the topics of general chemistry and geometry are acquired from the Khan Academy for demonstrating the feasibility of the proposed framework. Experimental results have shown that the framework can be used to achieve the intended goals in specific domains.

Index Terms—E-Learning, knowledge representation, online education, learning strategies.

The authors are with the Hong Kong University of Science and Technology Computer Science and Engineering Department, Clear Water Bay, Kowloon, Hong Kong (e-mail: leofpm@ust.hk, tcpong@ust.hk).


Cite:Pak-Ming Fan and Ting-Chuen Pong, "Constructing Knowledge Representation from Lecture Videos through Multimodal Analysis," International Journal of Information and Education Technology vol. 3, no. 3, pp. 304-309, 2013.

General Information

  • ISSN: 2010-3689 (Online)
  • Abbreviated Title: Int. J. Inf. Educ. Technol.
  • Frequency: Monthly
  • DOI: 10.18178/IJIET
  • Editor-in-Chief: Prof. Dr. Steve Thatcher
  • Executive Editor: Ms. Nancy Y. Liu
  • Abstracting/ Indexing: Scopus (CiteScore 2022: 2.0), INSPEC (IET), UGC-CARE List (India), CNKI, EBSCO, Google Scholar
  • E-mail: ijiet@ejournal.net


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