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研究生:蘇布赫
研究生(外文):Subhan Abdul Gani
論文名稱:WordNet-based Automatic Metadata Tagging of E-Learning Objects for improving Employee Competency
論文名稱(外文):WordNet-based Automatic Metadata Tagging of E-Learning Objects for improving Employee Competency
指導教授:蘇傳軍蘇傳軍引用關係
指導教授(外文):Chuan-Jun Su
口試委員:范書愷孫天龍
口試委員(外文):Shu-Kai S. FanTien-Lung Sun
口試日期:2013-08-23
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:102
語文別:英文
論文頁數:89
中文關鍵詞:E-learningReusabilityLearning Object MetadataLearning ObjectRepositoryMetadata Annotation
外文關鍵詞:E-learningReusabilityLearning Object MetadataLearning ObjectRepositoryMetadata Annotation
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  • 被引用被引用:0
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  • 收藏至我的研究室書目清單書目收藏:1
Public repositories provide unlimited e-learning resources for any educational and training purposes especially for leveraging human resources competency. At the same time it can also increase the reusability of e-learning resources, and in turn, will be able to reduce the cost significantly for the creation and maintenance on the e learning provider side. To facilitate sharing and reuse of LOs on global scale, IEEE LOM (Learning Object Metadata) has been developed for describing the pedagogical aspects of LOs, such as cataloging the LOs based on a taxonomic system of a particular knowledge domain for usage in any educational contexts. The metadata annotation of the LOs can be customized based on an organization need which can extend the usage coverage of the LOs for any particular organizations. This goal can be achieved by through semantically annotating the LO’s metadata.
In this research we aim to extend the reusability of the LOs by associating the LOs with subjects/topics within a pre-defined taxonomic tree. The similarity between topics is calculated using the Lexical Level Similarity approach with the WordNet as the lexical similarity base. This approach will categorize the similar topic within the LOs and a job task topic by measuring the similarity score between the textual meanings of LO’s title, descriptions and keywords with a particular job task’s title and description. Through this way, the LOs will have an association to a similar topic whenever the users browse a learning object repository in order to find the LOs of his interest. The similar topics, based on the similarity score, will be automatically annotated into the metadata of the related LO. This research is intended to evaluate the accuracy rate of the approach in associating the similar topic of LOs. Based on our experiment this approach gives high accuracy in inferring dissimilar topics rather than inferring the similar ones.
Public repositories provide unlimited e-learning resources for any educational and training purposes especially for leveraging human resources competency. At the same time it can also increase the reusability of e-learning resources, and in turn, will be able to reduce the cost significantly for the creation and maintenance on the e learning provider side. To facilitate sharing and reuse of LOs on global scale, IEEE LOM (Learning Object Metadata) has been developed for describing the pedagogical aspects of LOs, such as cataloging the LOs based on a taxonomic system of a particular knowledge domain for usage in any educational contexts. The metadata annotation of the LOs can be customized based on an organization need which can extend the usage coverage of the LOs for any particular organizations. This goal can be achieved by through semantically annotating the LO’s metadata.
In this research we aim to extend the reusability of the LOs by associating the LOs with subjects/topics within a pre-defined taxonomic tree. The similarity between topics is calculated using the Lexical Level Similarity approach with the WordNet as the lexical similarity base. This approach will categorize the similar topic within the LOs and a job task topic by measuring the similarity score between the textual meanings of LO’s title, descriptions and keywords with a particular job task’s title and description. Through this way, the LOs will have an association to a similar topic whenever the users browse a learning object repository in order to find the LOs of his interest. The similar topics, based on the similarity score, will be automatically annotated into the metadata of the related LO. This research is intended to evaluate the accuracy rate of the approach in associating the similar topic of LOs. Based on our experiment this approach gives high accuracy in inferring dissimilar topics rather than inferring the similar ones.
ABSTRACT iv
ACKNOWLEDGMENTS vi
CONTENTS vii
LIST OF FIGURES x
LIST OF TABLE xi
CHAPTER 1 INTRODUCTION 1
1.1. Motivation 1
1.2. Problem Description 2
1.3. Research Approach 5
1.4. Research Overview 6
CHAPTER 2 LITERATURE REVIEW 7
2.1. E-learning and Learning Objects Repository 7
2.2. IEEE LOM (Learning Object Metadata) specification 9
2.2.1. General category (1, IEEE LOM) elements 10
2.2.2. Classification category (9, IEEE LOM) elements 11
2.3. Content Packaging (CP) and IMS CP Information Model 13
2.4. Automatic Metadata Annotation Tool 14
2.5. WordNet 15
2.6. WordNet::Similarity 17
2.7. Text Categorization 19
2.8. Sentence Semantic Similarity Measures 20
2.9. Job Task Classification 22
CHAPTER 3 RESEARCH METHODOLOGY 24
3.1. Detail explanation of the Annotation Tool 24
3.2. Architecture of the Annotation Tool 28
3.2.1. Learning Object Repository (LOR) 28
3.2.2. Metadata analyzer and Annotation Module 29
3.3. Computing the sentence similarity 30
3.3.1. Pre-processing 30
3.3.2. Measuring Similarity 34
3.4. Experimental Design 36
3.4.1. Data Sets 36
3.4.2. Evaluation Criteria 37
3.4.3. Threshold determination 39
CHAPTER 4 EXPERIMENT AND IMPLEMENTATION 41
4.1. Experimental Setting 41
4.1.1. Pre-processing Phase 41
4.1.2. WordNet Similarity and LLM 44
4.2. A typical Example 44
4.2.1. LO metadata and Job Task 45
4.2.2. Pre-processing phase 46
4.2.3. LLM Similarity Comparison 49
4.3. Datasets 50
4.4. Performance Measure 52
4.5. Experimental Results 54
4.5.1. Training Datasets Performance 54
4.5.2. Testing Datasets Performance 55
4.5.3. Observation during the experiment 57
4.6. Result Analysis 58
4.6.1. Comparing Solely the Titles 58
4.6.2. More Information is better 59
4.6.3. Recall and Precision results 59
4.7. Automatic Tool Implementation 60
4.7.1. Scenario 1: A tool in authoring of the LO metadata 60
4.7.2. Demonstration of the tool in Scenario 1 62
4.7.3. Scenario 2: Searching the related LO’ topic 64
4.7.4. Demonstration of the tool in Scenario 2 65
4.7.5. Analysis of Algorithm complexity 66
4.7.6. Case-based Reasoning for Reducing Calculation Complexity 67
CHAPTER 5 CONCLUSIONS 69
5.1. Conclusions 69
5.2. Contributions 70
5.3. Limitations 70
5.4. Future Works 71
REFERENCES 72
APPENDICES 78
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