content classification and context based retrieval system for e learning
TRANSCRIPT
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Content Classification and Context-Based Retrieval System for E-Learning
Ankush Mittal , Pagalthivarthi V. Krishnan , Edward Altman
International Forum of Educational Technology & Society , 2006
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Outline
Introduction Automatic methodology for indexing of lecture
videos Formulation and analysis a state model for lectures Video indexing features Lecture video indexing
Experimental Result and applications Conclusion
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Introduction
Base on Singapore-MIT Alliance(SMA) video database.
This paper issue of defining and automatically classifying the semantic fragment.
Target on the e-learning materials that in raw form as video, audio, slides.
Discuss how fragments can be contextually used for personal learning.
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Automatic methodology for indexing of lecture videos
Main problem : bridging the semantic gap between raw video and high level information required by students. 1. Classify 2. Discover relations 3. Formation of a base for providing various users
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Formulation and analysis a state model for lectures
Temporal state model for lectures (Ex : Algorithm) Introduction Definitions & Theorems Theory Discussions Review Question and Answer Sub-Topic
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Formulation and analysis a state model for lectures (cont.)
The semantic analysis of raw video steps: 1. Extract low and mid level features. 2. Classify 3. Apply contextual info to determine higher level
semantic events. 4. Apply a set of high level constraints
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Video indexing features
Audio features Video features Text features
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Lecture video indexing
Deriving semantics from low-level features Rule for indexing the slide :
Category 1 : Definitions/Theorems Category 2 : Examples Category 3 : Proof Category 4 : Formulae
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Lecture video indexing (cont.)
Contextual searching Manually enter the topic name for each video clip
associated with the event
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Experimental Result and applications
Test the method on 26 lecture videos from Singapore-MIT Alliance course SMA5503.
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Experimental Result and applications (cont.)
Personalization Student interested in this course can be divided 3 categories :
1. viewing the lecture for the first time 2. reviewing to brush up concepts 3. reviewing for preparation of exam
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Experimental Result and applications (cont.)
Retrieving fragments of document
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Conclusion
video 分段的概念不錯 , 只是做法上的限制頗大
在 user tracking 的地方 , 不但可用一般sequence 來判斷他念過哪些 , 還可以藉由使用者的 review 次數 , 以及時間是否接近考試來回傳不同的資訊