將數位資訊融入教育的理念與數位學校終身學習的理想，廣泛引起政府、學術界和業界的重視並積極投入數位學習系統之研發，互動學習的功能為主要的探討問題之一。然而，目前的系統大多偏向靜態的教材呈現，動態的教材內容管理和學習輔助等技術尚未能滿足使用者的需求，造成重複性與盲目摸索的學習現象，嚴重影響學習成效而降低學習動機。
我們認為整合目前相關的數位學習技術，以模糊理論描述學習者之知識所具有不明確的特質，並以叢集技術分析學習者對於領域知識中概念理解之分佈和學習傾向，設計新的演算法輔助學習，引導學習者學習其他新的概念知識，將可有效改善使用者的滿意度。本研究使用符合國際標準規格的SCORM執行環境，以本體論表述教材知識領域之概念物件及物件間的階層性和關聯性；依據教師的教學策略和學習者與系統課程教材間互動之學習歷程，設計模糊集合演算法以多元化評量學習者對教材知識概念之學習能力，決定其學習知識概念之認知狀況；尤其，根據概念認知狀況在知識概念領域的相對位置，設計演算法擷取學習者之學習路徑集合；並利用叢集技術分類候選指導類型和決定適合學習之候選概念分類的優先順序，以推薦個人化課程教材輔助學習者。模擬並實驗以輔助模組引導三種不同能力之學習者學習領域知識，探討學習者之學習成績分別為均勻分佈和高斯分佈的狀況下，系統推薦學習物件內容的準確度並和隨機推薦法比較，以評估提出之模型和演算法。實驗結果顯示，本研究提出的模組不但能有效地幫助不同能力之學習者完成領域知識之學習，而且其推薦學習物件的準確度，遠優於隨機推薦法。本方法可望提供學習者更個人化的學習輔助系統與更精確的學習診斷結果，進而提升學習者對學習系統的滿意度。

The perception of incorporating digital information into online educational systems and the ideal of developing digital schools for lifelong learning have drawn much attention of the governments, academia, and industries around the world. The techniques of interactive learning have become a primary research topic in E-learning. However, most existing E-learning systems provide static instructional materials. The techniques of dynamic learning content management that adaptive to individual user knowledge level and learning goals have been tough challenges for the related research communities. The resulting repetitive and blind learning phenomena have significantly reduced user performance and motivation.
We hypothesized that new algorithms of adaptive learning based on the integration of current information technologies, the use of fuzzy theory to express the uncertainty features of the user knowledge, and the exploitation of clustering techniques to analyze the knowledge of a user for the comprehended areas of the domain knowledge will effectively improve user satisfaction. In this study, a prototype system is developed, implemented, and experimented by using SCORM run-time environment. The knowledge of teaching domain and the features of the learner behavior are modeled by ontology to represent the hierarchy and relationship of the learning concepts. To quantify user knowledge and learning ability, fuzzy sets are applied with multiple analysis dimensions based on the pedagogical strategies and user learning experiences. The performance of a user for learning knowledge concepts is then evaluated. In particular, an algorithm is designed to extract the existing learning paths of a user by the relative position of the concepts that the user attains in the domain knowledge. Furthermore, the candidate direction types for recommending concepts are inferred and the candidate learning concepts that are appropriate or inappropriate to learn followed up can be identified by rules. Moreover, the candidate learning concepts are scheduled to construct customizable learning routes by clustering techniques. The personalized learning contents that best matching user learning intention would then be presented to the user. Simulations study in the uniform and normal distributions for the grades of users is conducted to evaluate the tutoring model for three levels of users. The experimental results show that the proposed model helps different levels of users to learn the domain knowledge effectively and the accuracy of recommending the relevant learning object contents is superior than the random selection method. With a richer description of user knowledge and features, the proposed adaptive system for online learning assistance may better diagnose the understanding of a learner and enhance the pertinence of the retrieved courses to user intended learning to improve the service quality for the user.

CHAPTER 1 INTRODUCTION 1
CHAPTER 2 RELATED WORK 6
2.1 LEARNING DESIGN 6
2.2 JAVA LEARNING OBJECT ONTOLOGY 8
2.3 SCORM AND METADATA FOR LEARNING CONTENT 14
2.4 FUZZY USER MODEL 16
2.5 COURSE PERSONALIZATION 17
CHAPTER 3 THE PROPOSED TUTORING SYSTEM 18
3.1 INSTRUCTIONAL STRATEGY 22
3.2 LEARNING CONTENT 26
3.3 FUZZY USER MODEL 28
3.4 CONCEPT RECOMMENDATION AGENT 33
3.4.1 Assessing the User Knowledge 34
3.4.2 Extracting Existing Learning Paths 37
3.4.3 Inferring the Candidate Direction Types 42
3.4.4 Scheduling a Customizable Route 49
3.5 CONTENT SELECTION AGENT 58
CHAPTER 4 PERFORMANCE EVALUATION 66
4.1 EXPERIMENTAL ENVIRONMENT 67
4.2 SIMULATION RESULTS 69
4.2.1 Using a Uniform Distribution for the Grades of Users 70
4.2.2 Using a Normal Distribution for the Grades of Users 99
4.2.3 Discussion 105
CHAPTER 5 CONCLUSION 107
REFERENCES 108
APPENDIX THE DOMAIN KNOWLEDGE OF JAVA LEARNING OBJECT ONTOLOGY BUILT BY PROT

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[14] Prot