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系統識別號 U0026-0812200915114790
論文名稱(中文) 利用語意知識於個人化之動態課程推薦系統
論文名稱(英文) A Semantic-Aware Personalized Course Recommendation and Composition for e-Learning Systems
校院名稱 成功大學
系所名稱(中) 工程科學系碩博士班
系所名稱(英) Department of Engineering Science
學年度 97
學期 2
出版年 98
研究生(中文) 蔡昆樺
研究生(英文) Kun-hua Tsai
電子信箱 tsaikunhua@msn.com
學號 N9891102
學位類別 博士
語文別 英文
論文頁數 78頁
口試委員 口試委員-朱治平
指導教授-王宗一
口試委員-李建億
口試委員-孫光天
口試委員-李健興
中文關鍵字 本體論  個人化推薦  離散型粗粒子最佳化演算法  數位學習 
英文關鍵字 E-learning  Ontology  DPSO  Personalized recommendation 
學科別分類
中文摘要 隨著網路的蓬勃發展,以網頁為基礎的互動技術已經讓遠距數位學習系統的開發變得越來越可行也越來越普遍。遠距數位學習與傳統課堂學習間最大的差異在於遠距數位學習系統俱有容易達成個人化學習的優勢。然而在目前已開發的數位學習系統中,如何根據學習者的學習企圖動態產生合適的個人化課程仍是一大挑戰。因此,本論文提出一具語意感知的方法輔助數位學習系統了解學習者的學習企圖並進而推薦符合學習者偏好與學習企圖的個人化課程供其學習。此方法首先利用基於正規概念分析之半自動化課程知識建構機制建立課程的相關知識,之後,系統能利用預先建構的課程知識分析學習者的查詢語句並判斷出學習者真正的學習企圖為何。接著,學習者的學習企圖將被轉換成相對應的學習概念,而系統同時採取混合式推薦模型來推薦符合學習者偏好的學習元件作為個人化課程的候選內容,在最後課程組合的階段中,系統採取離散型的粗粒子群集最佳化演算法來提升從候選內容中挑選合適學習元件的速度與效能。此外為了使學習者在學習時有較好的閱讀順序,系統利用貪婪式排序演算法去組織合適的學習順序供學習者學習課程。從實驗中,結果顯示已推薦的個人化課程能滿足學習者的學習需求,並且分析學習者的回饋,也可發現推薦的學習元件都能盡可能地符合學習者的偏好。在系統效能方面,提出的離散型粗粒子最佳化演算法也被證實能有效地降低挑選學習元件的時間。
英文摘要 The energetic development of the Internet, especially on the web page interaction technology, has made distant e-learning systems become more and more realistic and popular in the past ten years. Problems due to technology shortcomings, however, gradually emerge when using current e-learning systems, among which, how to compose personalized courses dynamically in accordance with a user’s intention is the largest challenge. One of the advantageous prospects of e-learning systems comes from the easiness of achieving personalized learning, which is virtually impossible in traditional classrooms; but the lack of proper technologies has been blocking the dream from coming true. This thesis proposed a semantic-aware approach that makes an e-learning system able to infer a user’s query and then recommend a personalized course according to the user’s preference and intention. The proposed approach uses a semi-automatic ontology constructing mechanism, developed in this research, to build domain knowledge ontology of different courses. By using a constructed ontology, the approach can analyze a user’s query and understand what concepts in a specific domain the user is intending to learn. It then uses a hybrid recommendation model, developed also for the proposed approach, to recommend suitable learning objects according to a user’s preference and intention. In the last phase of the approach, the personalized course composition, an adapted discrete particle swarm optimization is used to promote the performance of picking suitable learning objects and a smooth reading order is constructed for the user’s comfortable learning and reading. From the experimental results, it shows that personalized courses dynamically composed by the proposed approach can satisfy different users’ needs with their feedbacks indicate that the recommended domain concepts conformed to their learning intentions and the picked learning objects fit their preferences.
論文目次 中文摘要 I
Abstract II
致謝 III
Contents IV
List of Tables VI
List of Figures VII
CHAPTER 1 Introduction 1
1.1 Motivation 1
1.2 Goals 3
1.3 Contributions 4
1.4 Organization of this dissertation 5
CHAPTER 2 Literature Review 6
2.1 Learning Object Metadata (LOM) 6
2.2 Ontology Overview 8
2.2.1 Ontologies and their applications 8
2.2.2 An ontology construction process 9
2.2.3 Semi-automatic approach for constructing the ontology 11
2.3 Recommendation Technologies 13
2.4 Particle Swarm Optimization Algorithm 15
2.5 Overview of the Learning Models 16
CHAPTER 3 Personalized Course Recommendation and Composition 21
3.1 Ontology Creator 23
3.2 Learning Objects Classifier 26
3.3 Query Parser 28
3.4 Learner Objects Recommender 34
3.4.1 Recommendation based on a user’s preference 35
3.4.2 Recommendation based on similar users 38
3.4.3 Integrated ranking strategy 41
3.5 Course Composer 42
3.5.1 Objective and fitness functions 42
3.5.2 Discrete particle swarm optimization (DPSO) 43
3.5.3 A Greedy-like algorithm for sequencing the reading order 52
3.6 Feedback Calculation 54
CHAPTER 4 Implementation and Performance Evaluating 55
4.1 Overview of the System Web Portal 55
4.2 Evaluation Measures 58
4.2.1 Recall and precision 58
4.2.2 Mean absolute error 59
4.3 Experimental Setting 60
4.4 Experiment Results and Discussion 61
4.4.1 Evaluate the variation of fitness values 61
4.4.2 Evaluations of recall, precision, and f-measure 62
4.4.3 Evaluate satisfied degree of the reading order 63
4.4.4 Recommendation based on feedbacks with different weights 65
4.4.5 Construction of a learner’s preference pattern 70
CHAPTER 5 Conclusions and Future Work 72
Bibliography 74
自述 78
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