Constructing Knowledge Representation From Lecture Videos Through Multimodal Analysis

MPhil Thesis Defence


Title: "Constructing Knowledge Representation From Lecture Videos Through
Multimodal Analysis"

By

Mr. Pak-Ming Fan


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 common learning materials on e-learning platforms. It
presents knowledge in a lively manner and keeps the learners more focused
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
relationship between the concepts so as to identify effective viewing
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 illustrating the feasibility of the proposed
framework. Experimental results have shown that the framework can be used
to achieve the goals under specific assumptions.


Date:			Friday, 31 August 2012

Time:			4:00pm – 6:00pm

Venue:			Room 3408
 			Lifts 17/18

Committee Members:	Prof. Ting-Chuen Pong (Supervisor)
 			Dr. Raymond Wong (Chairperson)
 			Dr. Chong-Wah Ngo (Comp. Sci., CityU)


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