隨著Web2.0的發展，資訊型態有了新的發展，使用者可主導創造、協同合作及分享各種資訊與內容。網路雲端上豐富且多元的資訊內容促使數位學習發展有了新的趨勢，創新的學習平台上應朝資訊跨平台，學習跨平台與服務跨平台等目標前進。過去Web 1.0的時代，是以內容為導向的學習教學模式，往往都是由教師製作教材後上傳至學習平台，學習者被動的接收老師灌輸的知識。故當學習遇到困難時，最常尋求網路資源尋找答案，諸如搜尋引擎、論壇、Wikipedia與YouTube等雲端內容資源。問題的查找，是一種知識內容發現與擴增的行為，倘若每個學習者均能將發現與擴增的知識內容分享於學習平台中，學習內容將不再單純由導師提供，而是經由學習者共同創造累積得來。故本論文在雲端學習平台上提出一個協作內容分享與媒合的服務，藉由Web 2.0的技術，開發以學習者為導向的服務、加強學習者個人創作、協同創作、社會網絡互動等機制。此服務整合網路雲端資源，藉此達到學習內容擴增，學習經驗分享、與有效率的學習方式。此外，平台亦提供運算資源，用以處理服務產生的計算分析，以提升系統效能與維持系統的穩定性。

With the development of Web 2.0, the Internet users can be led to creating, collaborating, and sharing all kinds of information and content. Rich and diverse contents on the Internet promote the development of learning to a new direction. An innovative learning platform should be designed as a cross-platform towards information, learning, and services. In the past era of Web 1.0, the learning model was content-oriented, and the teaching materials were often created and uploaded to the learning system by teachers, while students passively received the knowledge by studying such materials. When suffering from problems in learning, students often sought the Internet resources for answers, such as search engines, forums, Wiki, YouTube and etc. Such searching behaviors were kinds of knowledge discovery and expansion. When the discovered contents could be shared in the learning system, students would no longer simply be provided knowledge by teachers, but cooperatively created it with peers.

In this thesis, a collaborative content sharing and matching service (CCSMS) on cloud-based e-Learning platform is proposed. The service is user-oriented and developed with the technology of Web 2.0, which is expected to extend the contents by increasing the behaviors of content creation, sharing, and cooperation of students, enhancing the social network interaction, and integrating the cloud resources. More and more relevant contents are created and the usefulness is supportive of students’ learning. In addition, the platform supports computing resources for processing the large number of computing analyses generated by the proposed service for an efficient and stable environment.

Acknowledgments c
List of Tables iv
List of Figures vi
Chapter 1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Contribution of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Chapter 2 Related Works 8
2.1 e-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10
2.1.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.1.2 Learning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
2.2 Web Technology and e-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.1 Web 2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 e-Learning 2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 Social Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15
2.2.4 Mobile Learning and APP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Information Retrieval and e-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.1 Search Engine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Information Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3.3 Information Retrieval on e-Learning System . . . . . . . . . . . . . . . . . . . . . . 19
2.4 Distributed Computing System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.2 Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4.3 Cluster Computing v.s Grid Computing . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.4.4 Grid Computing v.s Cloud Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.5 Computing Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.5.1 Definition of Job Scheduling Problem . . . . . . . . . . . . . . . . . . . . . . . 23
2.4.5.2 Heuristic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.5.3 Population-Based Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4.5.4 Local Search Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5 Cloud-based e-Learning System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5.1 Cloud Computing on e-Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5.2 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5.3 Content Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
2.5.4 Concept-based Lecture Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Chapter 3 Collaborative Content Sharing and Matching Service (CCSMS) 32
3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.2 Concept Extraction Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3 Content Searching Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4 Similarity Computing Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Collaborative Contents Auditing Module . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.6 Collaborative Contents Ranking Module . . . . . . . . . . . . . . . . . .. . . . . . . . . . . 40
3.7 Job Scheduling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41
3.7.1 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.7.2 Heuristic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.7.3 Population-based Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Chapter 4 Example Services 47
4.1 Learning Diagnosis Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.2 Collaborative Content Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
4.3 Self-practice Examination Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
Chapter 5 Performance Evaluation 56
5.1 Service Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.1.1 Results in Terms of Accuracy Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.1.2 Results in Terms of Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.1.3 Results in Terms of F-measure Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 60
5.2 Experimental Results of Computing Problem . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.2.1 Environment and Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5.2.2 Experiments of Heuristic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
5.2.3 Experiments of Population-based Algorithm . . . . . . . . . . . . . . . . . . . . . . .72
5.2.3.1 Parameter Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
5.2.3.2 Experiment 1: One Cloud System . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
5.2.3.3 Experiment 2: Multiple Cloud System . . . . . . . . . . . . . . . . . . . . . . . 75
Chapter 6 Conclusion and Future Work 80
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
References 82
Appendix A Chinese to English Translation 95
Appendix B Schedule Results of Experiment 1 97

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