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研究生： 李明哲
Che, Ming
論文名稱： 具語意感知的個人化之學習元件擷取與推薦系統
A Semantic-Aware Framework for Personalized Learning Objects Retrieval & Recommendation
指導教授： 王宗一
Wang, T. I.
學位類別： 博士
Doctor
系所名稱： 工學院 - 工程科學系
Department of Engineering Science
論文出版年： 2007
畢業學年度： 95
語文別： 英文
論文頁數： 106
中文關鍵詞： 學習元件 、數位學習 、語意 、推薦
外文關鍵詞： Ontology, Reommendation, e-Learning, Semantic, Learning Object
相關次數： 點閱：121  下載：2 
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    • 「數位學習」指的是以數位工具，透過有線或無線網路，取得數位教材，進行線上或離線之學習活動。因而數位學習產業涵括數位學習工具（載具及輔具）研發、數位學習網路環境建置、數位教材內容開發、以及數位學習活動的設計等。目前國際上已有許多流通的標準，其中針對"學習元件(Learning Object)"與"學習元件元資料(Learning Object Metadata, LOM)"，在國際間最具份量的標準為美國國防部ADL 的SCORM(Sharable Content Object Reference Model)的規格，此規格已被國內各研究單位與產業界廣泛採用。
    SCORM-LOM是一種”描述學習元件資料”的資料，其制定的目的便是希望每一個共享的學習元件可被有效的描述與搜尋。但LOM的標籤只是一種靜態的描述格式，在共享學習元件搜尋系統的設計上，若只用此分類當作搜尋的依據，則此搜尋系統只具備關鍵字比對(keyword match)的能力，缺乏語意搜尋(semantic search)與檢索推論(query reasoning)的能力。且LOM所制定的元資料的項目數量過於龐大，原則上很難要求使用者具備這些背景知識。另一方面，若系統只提供上層LOM欄位供學習者檢索，則搜尋數量可能過於龐大而缺乏精確性。有鑑於上述教學元件元資料在實際搜尋應用上的困難，本論文設計了一高效能且具備語意搜尋與檢索推論能力之共享學習元件搜尋推薦系統。在使用者的檢索語句設計上，有鑒於使用者未必具被所需之學習元件的背景知識，因此其所輸入的檢索詞彙未必是最適合使用者的需求。在此我們將使用LOM辭書的概念階層樹，配合查詢擴展(Query Expansion)與學習元件協同推薦演算法，將使用者的查詢檢索結果做最精確的調整。

    With vigorous development of Internet, especially the web page interaction technology, distant e-learning has become more and more realistic and popular. SCORM LOM, i.e. the Learning Object Metadata, enables the indexing and searching of learning objects in a learning object repository by extended sharing and searching features. However, LOM has a deficiency in semantic-awareness capability. Most LOM-based learning object retrieval mechanisms just provide keyword-based search. This thesis proposes an ontology-based framework for establishing personalized learning objects retrieval and recommendation. The personalization functionality is provided by the probabilistic semantic inferring of query terms, LOM-based user preference, and collaborative feedback. An ontology query expansion algorithm and an integrated learning objects ranking algorithm are proposed. Focused on digital learning material and contrasted to other traditional keyword-based search technologies, the proposed approach has shown significant improvement in retrieval precision, recall rate, and ranking performance.

    摘要 i Abstract ii 致謝 iii Contents vi List of Tables vii List of Figures viii Chapter 1 Introduction 1 1.1 Background - IEEE 1484.12.1 Learning Object Metadata Standard 2 1.2 Ontology Concepts and Semantic Web 4 1.2.1 Ontology concepts 4 1.2.2 Ontology & semantic web 5 1.2.3 A survey of ontology tools and ontology information systems 7 1.2.3.1 Ontology tools 8 1.2.3.2 Ontology information systems 9 1.3 The Lack of Semantic Support in e-Learning Paradigm 9 Chapter 2 The Structure of Course Ontology – A Case Study on Java Programming Language 12 2.1 Methodologies for Course Ontology Development 16 2.1.1 Skeletal methodology 16 2.1.2 KACTUS 16 2.1.3 Methodology 17 2.1.4 Purpose-oriented model 17 2.2 The Structure of JAVA Learning Object Ontology 20 2.3 Classes and Class Hierarchy 21 2.4 Definition of Slots 27 2.4.1 PREREQUISITES 27 2.4.2 MUST-TAKE and OPTION-TAKE 29 Chapter 3 Personalized Learning Objects Retrieval and Recommendation Framework 31 3.1 The Framework Major Modules 31 3.2 The Ontology Query Expansion Algorithm 33 3.2.1 Constructing User Intention Tree (UIT) 33 3.2.2 A Simple Scenario for Constructing the UIT 38 3.2.3 Expanding Query Terms from UIT 42 3.3 Integrated Learning Objects Ranking Algorithm 49 3.3.1 LOM-based Learner Preference 49 3.3.2 Collaborative Feedback 50 3.3.3 Integrated Ranking Strategy 51 3.4 Automatic Learning Objects Classification Algorithm 55 3.4.1 Ontology Concept Selection and Automatic Weight Adjustment Core Function 56 Chapter 4 Implementation and Performance Evaluating 63 4.1 Introduction to Implementation Toolkits and System Environment 64 4.1.1 Ontology editor – Protg 2000 64 4.1.2 Jena Semantic Web Framework 64 4.1.3 The Eclipse SDK version 2.1.3 65 4.2 Introduction to System Web Portal 66 4.3 Performance Evaluating 71 4.3.1 Upper & Domain ontology 71 4.3.2 Learning Objects 71 4.4 Performance Evaluating 74 4.4.1 Performance evaluating for ontology query expansion and ranking strategy 74 4.4.1.1 Evaluation metrics 74 4.4.1.2 Experimental results 76 4.4.2 Performance Evaluation for Automatic Learning Objects Classification Algorithm 83 4.4.2.1 System parameters tuning 83 4.4.2.2 Comparing with a traditional classification approach and other ontology-based approach 83 Chapter 5 Conclusion and Future Work 86 Bibliography 88 自述 93

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