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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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    • 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 <ul><li>Introduction </li></ul><ul><li>Automatic methodology for indexing of lecture videos </li></ul><ul><ul><li>Formulation and analysis a state model for lectures </li></ul></ul><ul><ul><li>Video indexing features </li></ul></ul><ul><ul><li>Lecture video indexing </li></ul></ul><ul><li>Experimental Result and applications </li></ul><ul><li>Conclusion </li></ul>
    • 3. Introduction <ul><li>Base on Singapore-MIT Alliance(SMA) video database. </li></ul><ul><li>This paper issue of defining and automatically classifying the semantic fragment. </li></ul><ul><li>Target on the e-learning materials that in raw form as video, audio, slides. </li></ul><ul><li>Discuss how fragments can be contextually used for personal learning. </li></ul>
    • 4. Automatic methodology for indexing of lecture videos <ul><li>Main problem : bridging the semantic gap between raw video and high level information required by students. </li></ul><ul><ul><li>1. Classify </li></ul></ul><ul><ul><li>2. Discover relations </li></ul></ul><ul><ul><li>3. Formation of a base for providing various users </li></ul></ul>
    • 5. Formulation and analysis a state model for lectures <ul><li>Temporal state model for lectures (Ex : Algorithm) </li></ul><ul><ul><li>Introduction </li></ul></ul><ul><ul><li>Definitions & Theorems </li></ul></ul><ul><ul><li>Theory </li></ul></ul><ul><ul><li>Discussions </li></ul></ul><ul><ul><li>Review </li></ul></ul><ul><ul><li>Question and Answer </li></ul></ul><ul><ul><li>Sub-Topic </li></ul></ul>
    • 6. Formulation and analysis a state model for lectures (cont.) <ul><li>The semantic analysis of raw video steps: </li></ul><ul><ul><li>1. Extract low and mid level features. </li></ul></ul><ul><ul><li>2. Classify </li></ul></ul><ul><ul><li>3. Apply contextual info to determine higher level semantic events. </li></ul></ul><ul><ul><li>4. Apply a set of high level constraints </li></ul></ul>
    • 7. Video indexing features <ul><li>Audio features </li></ul><ul><li>Video features </li></ul><ul><li>Text features </li></ul>
    • 8. Lecture video indexing <ul><li>Deriving semantics from low-level features </li></ul><ul><li>Rule for indexing the slide : </li></ul><ul><ul><li>Category 1 : Definitions / Theorems </li></ul></ul><ul><ul><li>Category 2 : Examples </li></ul></ul><ul><ul><li>Category 3 : Proof </li></ul></ul><ul><ul><li>Category 4 : Formulae </li></ul></ul>
    • 9. Lecture video indexing (cont.) <ul><li>Contextual searching </li></ul><ul><ul><li>Manually enter the topic name for each video clip associated with the event </li></ul></ul>
    • 10. Experimental Result and applications <ul><li>Test the method on 26 lecture videos from Singapore-MIT Alliance course SMA5503. </li></ul>
    • 11. Experimental Result and applications (cont.) <ul><li>Personalization </li></ul><ul><ul><li>Student interested in this course can be divided 3 categories : </li></ul></ul><ul><ul><ul><li>1. viewing the lecture for the first time </li></ul></ul></ul><ul><ul><ul><li>2. reviewing to brush up concepts </li></ul></ul></ul><ul><ul><ul><li>3. reviewing for preparation of exam </li></ul></ul></ul>
    • 12. Experimental Result and applications (cont.) <ul><li>Retrieving fragments of document </li></ul>
    • 13. Conclusion <ul><li>video 分段的概念不錯 , 只是做法上的限制頗大 </li></ul><ul><li>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 </li></ul>

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