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研究生:周建智
研究生(外文):Chien-Chih Chou
論文名稱:以本體論為基礎之普適學習格網
論文名稱(外文):An Ontology-based Ubiquitous Learning Grid
指導教授:廖慶榮廖慶榮引用關係
指導教授(外文):Ching-Jung Liao
學位類別:碩士
校院名稱:中原大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:英文
論文頁數:117
中文關鍵詞:學習管理系統格網服務本體論普適學習學習格網
外文關鍵詞:Learning GridGrid ServiceUbiquitous LearningLearning Management SystemOntology
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日本與南韓在2004年分別提出「u-Japan」與「u-Korea」,其中u代表的是無所不在的(ubiquitous)意思,並預期分別於2010年與2007年達成目標,將營造未來成為架構於無所不在的資訊社會。因此有學者開始提倡所謂透過無所不在的運算(Ubiquitous Computing)的各式行動運算裝置,來達成普適學習(u-Learning)的概念,積極地表態將傳統的數位學習轉化成為無所不在的學習模式。本研究所提出的具本體論之普適學習格網乃是因應無所不在的學習,且目前的學習管理系統(Learning Management System, LMS)除了學習物件(Learning Object, LO)大多遵守SCORM的標準外,不同的學習管理系統間,其學習的服務與流程並未被SCORM所規範,這也使得分散在不同學習系統的學習服務難以相互分享。因此,學習管理系統和網路建設雖然普遍,但仍存在著兩個問題;一個是各學習管理系統間的學習服務往往不相容,以致於難以共享;另一個是學習內容無法針對各種終端裝置及個別使用者的偏好來動態提供,亦即無法達到普適學習的基本目的。

本研究的目的是在普適學習格網的環境中加入適合的本體論,並稱此架構為「以本體論為基礎之普適學習格網(Ontology-based Ubiquitous Learning Grid, OULG)」,來達成無縫式的學習環境。所謂普適學習格網則是透過無所不在的運算環境,在任何時間、任何地點與任何設備,由系統加以推論及判斷,來提供學習者適切的學習內容與程序。目前的學習管理系統相對於脈絡感知(context awareness)能力的描述則較少。格網服務是在格網計算中用來構成一個學習服務的方法,因此我們使用格網服務將學習物件和學習流程進行整合來構成學習服務。以格網服務來達成適性化學習,重點在於如何進行學習調適的部份。所謂學習調適指的是針對學習過程的輸入和輸出來進行適性化的調整,因此在普適學習格網上加入本體論可以加強調適的能力。由於本體論是一個使用受到管控的階層式辭彙來描述知識的結構,用以將概念的本體抽象化,來區分不同的類別與個體,並可定義在概念之間的關係。所以在本研究中,主要是使用一組本體論來加強在普適學習格網上無縫式學習的能力。由於學習管理系統的課程素材製作昂貴且冗長,而嵌入在學習管理系統中的元件也很少被共享及再利用,因此我們使用「學習本體論(Learning Ontology, OntoL)」來解決這類的問題。此外,透過「脈絡感知本體論(Context Awareness Ontology, OntoCA)」來對新裝置進行判斷,並參考對學習者的偏好設定,在學習內容輸出時將之自動轉換,以提供學習者適合的學習內容與程序。

由於要達成普適學習的目的,學習者必須能透過各種不同的裝置或設備,來連接至格網服務的入口網站。而格網服務的入口網站則擔任服務仲介者(broker)的角色,並透過「脈絡感知本體論」的定義,針對各個連接上的裝置,來判別終端裝置的種類以及各種屬性,以決定提供的學習內容。我們使用「學習本體論」來管理與學習內容有關的介面以及定義學習服務與學習素材,並且使得兩者中間有所關聯;這將使得學習服務與學習素材間的使用上更加靈活,變化性也較高。實作系統後與實驗結果顯示OULG是可行且具有輔助學習上的效果。

這個研究提出了以本體論為基礎之普適學習格網,用以解決在無所不在的學習環境中針對各種不同學習者、裝置以及時空因素間調適上的困難,也藉由本體論在學習領域、任務,以及裝置、背景資訊感知等方面進行判斷與調適,在各種情境之下能提供學習者更適切的學習內容。
In 2004, Japan and Korea proposed u-Japan and u-Korea project, respectively, where “u” stands for ubiquitous, and expected to complete its implementation by 2010 and 2007, respectively, as the gateway towards ubiquitous information society. In parallel, some scholars, advocating transforming traditional electronic learning to ubiquitous learning, propose realizing the concept of u-Learning through the use of various mobile devices for ubiquitous computing. The Ontology-based Ubiquitous Learning Grid proposed in this research is aim to achieve ubiquitous learning. On the other hand, although the components of most current learning management systems conforms to SCORM standards, SCORM standards do not govern service and learning procedures between learning management systems, which makes learning services on different learning systems cannot share and communicate. In this sense, although learning management system and network infrastructure are widespread, there exists two main problems: incapability between learning management systems, which makes sharing difficult, and the fact that learning material can not be delivered to different types of client devices and to different learner preferences, which fails the fundamental objectives of ubiquitous learning.

The purpose of this research is to incorporate suitable Ontology into Ubiquitous Learning Grid to achieve seamless learning environment. Ubiquitous Leaning Grid uses ubiquitous computing environment to infer and determine the most suitable learning contents and procedures in anytime, any place and with any device. Current learning management systems have less description about context awareness description. Grid service is a method in a Grid Computing that comprises learning service. The key issue about achieving adaptive learning with Grid service is how to adapt learning. Adapting learning refers to making adjustment based on the input and output of a learning process, therefore, adding Ontology to Ubiquitous Learning Grid is a way to strengthen adaptiveness. Ontology uses a controlled hierarchy of terminology to describe knowledge structure, abstracting the Ontology of concepts, and to distinguish different categories and individual items and can also define the relation between concepts. Therefore in this research, a set of Ontology is used to enhance capability of the Ubiquitous Learning Grid in the aspect of seamless learning. Since the production of learning material could be costly and the components incorporated into learning management are rarely shared and reused, Learning Ontology, or OntoL is used, to rectify this drawback. In addition, Context Awareness Ontology, or OntoCA is used to detect new client devices and, with the reference to learner preference settings, the system can and automatically convert learning material to be more user-friendly.

To achieve the goal of ubiquitous learning, the system should handle learners connecting to the Grid service portal with different devices and equipments. Grid service portal functions as broker and, through the definition from Context Awareness Ontology, identifies client device and its properties to determine learning content. In this research, Learning Ontology is used to manage and associate learning interface and define learning material, this makes learning service and learning material more flexible. Through implementation of the system and experiment results, it has been demonstrated that OULG is feasible and effective in facilitating learning.

The research propose using Ontology-based Ubiquitous Learning Grid to resolve the difficulties concerning how to adapt learning environment for different learners, devices and places, and through Ontology identifying and adapting in the aspects of domain, task, device and background information awareness, suitable learning content can be delivered.
Table of Contents
摘 要 i
Abstract iii
致謝辭 v
Acknowledgements vii
Table of Contents ix
List of Figures xiii
List of Tables xv
Chapter 1 Introductions 1
1.1 Motivation for the Research 1
1.2 Research Objectives 2
1.3 Research Problems 3
1.4 Unanswered Questions 3
1.5 Scope of the Research 4
1.6 Limitations of the Research 5
1.7 Structure of the Thesis 6
Chapter 2 Related Works 9
2.1 Overview of Related Works 9
2.2 Grid Technologies 11
2.2.1 Grid Computing 11
2.2.2 Web Service 12
2.2.3 Grid Service 12
2.3 Learning Platform 14
2.3.1 Sharable Content Object Reference Model 14
2.3.2 Learning Management System 15
2.3.3 Learning Content Management System 15
2.4 Evolution of Ubiquitous Learning 16
2.4.1 Electronic Learning and Mobile Learning 16
2.4.2 Pervasive Learning 16
2.4.3 Ubiquitous Learning 17
2.5 Ubiquitous Learning Grid using CC/PP 18
2.5.1 Resource Description Framework 18
2.5.2 Composite Capability/Preference Profiles 19
2.5.3 Ubiquitous Learning Grid 20
2.6 Ontology-based Ubiquitous Learning Grid 21
2.6.1 Ontology 21
2.6.2 Web Ontology Language 22
2.6.3 Ubiquitous Learning Grid with Ontology 23
Chapter 3 System Framework and Research Method 27
3.1 System Architecture 27
3.1.1 Presentation Layer 27
3.1.2 Interface Adaptation Layer 28
3.1.3 Ontology Layer 28
3.1.4 Content Layer 29
3.2 Research Method 29
3.2.1 Prototyping Development 29
3.2.2 Learning Adaptation by Ontology 32
Chapter 4 Experimental Design and Results 43
4.1 System Environment and Implementation 43
4.2 Ontology Design 51
4.3 Scenario Simulation 60
4.4 Simulation Results and Discussion 66
Chapter 5 Conclusions and Future Work 77
5.1 Conclusions 77
5.2 Future Work 78
References 79
Appendix: Ontology Models of the Research 83
作者簡歷 99
VITA 101

List of Figures
Figure 2-1: Structure of related works of this research 10
Figure 3-1: System architecture of OULG 27
Figure 3-2: Scenario of conceptual design 30
Figure 3-3: Logical architecture of the OULG 32
Figure 3-4: Illustration of the OULG works with Ontology 34
Figure 3-5: Asserted model of Preference Ontology 36
Figure 3-6: Inferred model of Preference Ontology 38
Figure 4-1: Operation description for system implementation 48
Figure 4-2: A set of Ontology in this research 51
Figure 4-3: Ontology development with Protégé 52
Figure 4-4: Asserted model of Domain Ontology 54
Figure 4-5: Statements of Domain Ontology 56
Figure 4-6: Inferred model of Domain Ontology 58
Figure 4-7: The way of interface communication between Ontology 59
Figure 4-10: Portal of OULG prototype 66
Figure 4-11: Screenshot of the OULG after login first time 67
Figure 4-12: An example of OULG getting recommended lectures and procedure 69
Figure 4-13: An example of adapted learning content provided by OULG 70
Figure 4-14: An example of adapted learning provided by OULG 71
Figure 4-15: An example of learning records in OULG 72
Figure 4-16: An example of logging to OULG with PDA 73
Figure 4-17: An example of OULG to obtain recommended lectures using PDA 74
Figure 4-18: An example of OULG obtaining adapted learning content 75
Figure A-1: Asserted model of Task Ontology 84
Figure A-2: Statements of Task Ontology 85
Figure A-3: Inferred model of Task Ontology 86
Figure A-4: Asserted model of Service Composition Ontology 87
Figure A-5: Statements of Service Composition Ontology 88
Figure A-6: Inferred model of Service Composition Ontology 89
Figure A-7: Asserted model of Device Ontology 90
Figure A-8: Statements of Device Ontology 91
Figure A-9: Inferred model of Device Ontology 92
Figure A-10: Asserted model of Preference Ontology 93
Figure A-11: Statements of Preference Ontology 94
Figure A-12: Inferred model of Preference Ontology 95
Figure A-13: Asserted model of Adaptation Ontology 96
Figure A-14: Statements of Adaptation Ontology 97
Figure A-15: Inferred model of Adaptation Ontology 98

List of Tables
Table 3-1: Example of parameters obtained by automatic gathered 34
Table 3-2: Example of parameters obtained from user’s input 35
Table 3-3: Statements of Skill.MIS 35
Table 3-4: Statements of Keyword.Skill.MIS 36
Table 3-5: Statements of Capability.Grid.GridConcept 37
Table 4-1: System implementation allocation of this research 44
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