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Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
Content  Classification And  Context  Based  Retrieval  System For  E  Learning
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Content Classification And Context Based Retrieval System For E Learning

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  • Transcript

    • 1. Content Classification and Context-Based Retrieval System for E-Learning Ankush Mittal , Pagalthivarthi V. Krishnan , Edward Altman International Forum of Educational Technology & Society , 2006
    • 2. 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
    • 3. 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.
    • 4. 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
    • 5. 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
    • 6. 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
    • 7. Video indexing features
      • Audio features
      • Video features
      • Text features
    • 8. 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
    • 9. Lecture video indexing (cont.)
      • Contextual searching
        • Manually enter the topic name for each video clip associated with the event
    • 10. Experimental Result and applications
      • Test the method on 26 lecture videos from Singapore-MIT Alliance course SMA5503.
    • 11. 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
    • 12. Experimental Result and applications (cont.)
      • Retrieving fragments of document
    • 13. 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

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