個人化的服務不再只是加值的服務，而是一個必須具備的基本功能，尤其是在線上學習系統上。目前的推薦系統在推薦課程給學習者時，大部分都只考慮到學習者的興趣或是偏好，往往都忽略了學習者的能力也是一項很重要的考量因素。在網路上的學習，由於超連結的網頁結構，造成使用者容易迷失在其中，而認知超載，資訊超載，缺乏適性機制和課程難度的調整都是主要的問題。所以本論文在建構一個人化的線上學習系統，能在學習者的學習過程中，動態地依據學習者的回應來評估學習者的能力，並將適合學習者能力之教材呈現給學習者來試圖解決上述問題，本研究利用試題反應理論達到上述的功能，並探討其適用的可行性；再者，我們也提供合作投票的方式來達到課程困難度的調整，用此方法，可以降低專家定義的難易度不符合一般大眾的認知差距，也降低可能因為少數人亂答而影響到課程困難度的正確性，以確保課程困難度的合理性及正確性。實驗顯示，利用試題反應理論，可以得到個人化學習的效果，以達成因材施教及循序學習的目的。

Personalized service is an important issue on the web, especially for web-based learning. Generally speaking, most personalized systems do not consider users’ abilities as an important factor to implement personalized mechanism and they only consider users’ preferences, interests, and browsing behaviors. Besides, too many link structures bring a lot of burdens to users. Hence, in web-based learning, disorientation (losing in hyperspace), cognitive overload, lack of adaptive mechanism, the information overload problem, and the adaptation of course materials difficulties are main research issues. Thus, we consider both the difficulties of course materials and users’ abilities to provide personalized learning. This thesis adopts Item Response Theory (IRT) to estimate the users’ abilities and rank appropriate course materials to users. We also propose the collaborative voting approach to the difficulties adjustment of the course materials in order to relieve the above-mentioned problems. Furthermore, the difficulties of the course materials and users’ abilities can be adjusted according to users’ explicit feedback to achieve the goal of the personalized course materials. The precision of the difficulties of course materials can be increased with the collaborative voting approach. Experimental results show that IRT applied to web learning can achieve personalized learning, and assist users to learn effectively and efficiently.

Contents
摘要 I
ABSTRACT II
誌謝 III
ACKNOWLEDGEMENTS IV
CONTENTS V
LIST OF FIGURES VIII
LIST OF TABLES X
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 CHALLENGES OF WEB-BASED LEARNING 4
1.3 OUR GOALS AND DESIGN 6
1.4 OUTLINE OF THE THESIS 6
CHAPTER 2 BACKGROUND 8
2.1 APPLICATIONS OF PERSONALIZATION 8
2.1.1 Content-Based Approach 8
2.1.2 Collaborative Approach 10
2.1.3 Hybrid Approach 11
2.2 COMPUTERIZED ADAPTIVE TESTING 12
2.2.1 Overview 12
2.2.2 Structure 14
2.3 ITEM RESPONSE THEORY 17
2.3.1 Item Characteristic Curve 18
2.3.2 The Assumptions of IRT 19
2.3.3 Item Response Model 21
2.3.4 User’s Ability Estimation 24
2.3.5 The Methods of Item Selection 27
2.4 TECHNOLOGIES OF WEB-BASED EDUCATION SYSTEMS 30
2.4.1 Intelligent Tutoring Systems 30
2.4.2 Adaptive Hypermedia Systems 31
CHAPTER 3: PERSONALIZED COURSE RECOMMENDATION SYSTEM 34
3.1 SYSTEM ARCHITECTURE 34
3.2 INTERFACE AGENT 40
3.3 IRT AGENT 41
3.3.1 Feedback Agent 42
3.3.2 Courses Recommendation Agent 49
3.4 THE PROPERTY OF OUR PROPOSED COURSE RECOMMENDATION SYSTEM 51
CHAPTER 4 EXPERIMENTS AND ANALYSIS 52
4.1 EXPERIMENTAL ENVIRONMENT 52
4.2 EXPERIMENTAL RESULTS AND ANALYSIS 62
4.2.1 The Difficulties Adjustment of Course Materials 62
4.2.2 The Adaptation of Users’ Abilities 66
4.3 SATISFACTION EVALUATION 80
4.4 EXPERIMENTAL CONCLUSION 85
CHAPTER 5 CONCLUSION 86
5.1 DISCUSSION 86
5.2 CONCLUSION 89
5.3 FUTURE WORK 90
REFERENCES 93
VITA 103
List of Figures
FIGURE 2.1. GENERAL STRUCTURE OF CAT. 15
FIGURE 2.2. FLOWCHART OF AN ADAPTIVE TEST. 16
FIGURE 2.3. THE ITEM CHARACTERISTIC CURVE. 19
FIGURE 2.4. THE DEVELOPMENT PROCESS OF RASCH MODEL. 22
FIGURE 2.5. THE THREE KINDS OF TYPES ABOUT IRT MODEL. 24
FIGURE 3.1. PEL-IRT ARCHITECTURE. 36
FIGURE 3.2. DATABASE SCHEMA OF PEL-IRT. 38
FIGURE 3.3. THE PROCESS OF PEL-IRT. 40
FIGURE 3.4. OPERATION FLOWCHART OF FEEDBACK AGENT. 43
FIGURE 3.5. OPERATION FLOWCHART OF COURSES RECOMMENDATION AGENT. 49
FIGURE 4.1. THE FRONT-PAGE OF THE PEL-IRT SYSTEM. 52
FIGURE 4.2. BROWSING THE COURSE MATERIALS WITHOUT LOGIN. 53
FIGURE 4.3. LOGIN PROCEDURE FOR PERSONALIZED RECOMMENDATION. 53
FIGURE 4.4. AN EXAMPLE OF RESPONSE TO USER’S QUERY IN A UNIT OF A CATEGORY. 55
FIGURE 4.5. AN EXAMPLE AFTER USER CLICKS ONE COURSE MATERIAL. 56
FIGURE 4.6. RECOMMENDATION LIST AFTER USER RESPONDS THE QUESTIONNAIRE. 57
FIGURE 4.7. ONE COURSE MATERIAL SELECTED FROM THE RECOMMENDATION LIST. 58
FIGURE 4.8. THE SECOND RECOMMENDATION LIST. 58
FIGURE 4.9. COMPARISON BETWEEN THE RECOMMENDATION LISTS AT FIRST TIME AND SECOND TIME. 60
FIGURE 4.10. COMPARISON BETWEEN THE RECOMMENDED COURSE MATERIALS LISTS OF THREE TYPES OF ABILITIES. 61
FIGURE 4.11. THE TUNED CURVES THE DIFFICULTIES OF COURSE MATERIALS. 66
FIGURE 4.12. THE ADAPTATION OF USERS’ ABILITIES. 67
FIGURE 4.14. THE RELATIONSHIP BETWEEN THE RANGE OF THE ADJUSTMENT OF USER A’S ABILITY AND THE DIFFICULTIES OF CLICKED COURSE MATERIALS. 70
FIGURE 4.15. THE RELATIONSHIP BETWEEN USER A’S ABILITY AND THE DIFFICULTIES OF RECOMMENDED COURSE MATERIALS. 71
FIGURE 4.16. THE RELATIONSHIP BETWEEN THE RANGE OF ADJUSTED USER A’S ABILITY AND THE DIFFERENCE OF DIFFICULTIES OF RECOMMENDED COURSE MATERIALS. 72
FIGURE 4.17. THE RELATIONSHIP BETWEEN THE RANGE OF ADJUSTED USER A’S ABILITY AND THE DIFFERENCE OF DIFFICULTIES OF RECOMMENDED AND CLICKED COURSE MATERIALS. 72
FIGURE 4.18. THE RELATIONSHIP BETWEEN THE ADJUSTMENT OF THE USER B’S ABILITY AND THE DIFFICULTIES OF CLICKED COURSE MATERIALS. 77
FIGURE 4.19. THE RELATIONSHIP BETWEEN THE RANGE OF THE ADJUSTMENT OF USER B’S ABILITY AND THE DIFFICULTIES OF CLICKED COURSE MATERIALS. 77
FIGURE 4.20. THE RELATIONSHIP BETWEEN USER B’S ABILITY AND THE DIFFICULTIES OF RECOMMENDED COURSE MATERIALS. 78
FIGURE 4.21. THE RELATIONSHIP BETWEEN THE RANGE OF ADJUSTED USER B’S ABILITY AND THE DIFFERENCES OF DIFFICULTIES OF RECOMMENDED COURSE MATERIALS. 79
FIGURE 4.22. THE RELATIONSHIP BETWEEN THE RANGE OF ADJUSTED USER B’S ABILITY AND THE DIFFERENCES OF DIFFICULTIES OF RECOMMENDED AND CLICKED COURSE MATERIALS. 79
List of Tables
TABLE 4.1 DESCRIPTION OF FIELD NAMES OF DIFFICULTY ADJUSTMENT 63
TABLE 4.2 THE DIFFICULTY ADJUSTMENT OF COURSE A 64
TABLE 4.3 THE DIFFICULTY ADJUSTMENT OF COURSE B 64
TABLE 4.4 THE DIFFICULTY ADJUSTMENT OF COURSE C 65
TABLE 4.5 DESCRIPTION OF FIELD NAMES OF USER’S ABILITY ADJUSTMENT 68
TABLE 4.6 LEARNING PROCESS OF USER A 69
TABLE 4.7 LEARNING PROCESS OF USER B 74
TABLE 4.8 THE MAPPING OF COURSE MATERIALS 75
TABLE 4.8 THE MAPPING OF COURSE MATERIALS (CONTINUE) 76
TABLE 4.9 THE USER SATISFACTION ABOUT RECOMMENDED COURSE MATERIALS DURING LEARNING PROCESS 82
TABLE 4.10 THE USER SATISFACTION AFTER LEARNING 84

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URL lists:
[100] AltaVista, Available at http://www.altavista.com.
[101] Amazon, Available at http://www.amazon.com.
[102] CNN, Available at http://www.cnn.com.
[103] Direct Hit, 2000, Available at http://www.directhit.com.
[104] Distance Learning Resources Network (DLRN),
Available at http://www.dlrn.org.
[105] eBay, Available at http://ebay.com.
[106] Education Cities, Available at http://www.educities.edu.tw.
[107] E-learning in Nation Tsing Hua University,
Available at http://elearn.cc.nthu.edu.tw.
[108] E-learning in Cisco, Available at http://www.cisco.com.
[109] GMAT, Available at http://www.gmat.org.
[110] GRE, Avaiable at http://www.gre.org.
[111] Good2U, Availiable at http://www.good2u.com.
[112] Google, Available at http://www.google.com.
[113] International Data Corporation, Available at http://www.idc.com.
[114] NEC Research Institute ResearchIndex,
Available at http://citeseer.nj.nec.com.
[115] The investigation of Yam, Available at http://survey.yam.com.
[116] Yahoo, Available at http://www.yahoo.com.
[117] YAM, Available at http://www.yam.com.
[118] Taiwannews, Available at http://www.etaiwannews.com.
[119] TOEFL, Avaliable at http://www.toefl.org.
[120] Formosa TV, Available at http://www.ftvn.com.tw.