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研究生:蔡昌均
研究生(外文):Chang-Jiun Tsai
論文名稱:電腦輔助教學專家系統中知識管理之研究
論文名稱(外文):A Study of Knowledge Management of Computer-Assisted Instruction Expert System
指導教授:曾憲雄曾憲雄引用關係
指導教授(外文):Shian-Shyong Tseng
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊科學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:英文
論文頁數:135
中文關鍵詞:電腦輔助教學規則式專家系統適性化教學知識管理知識表達知識擷取知識分析
外文關鍵詞:Computer-Assisted InstructionRule-based Expert SystemAdaptive InstructionKnowledge ManagementKnowledge RepresentationKnowledge AcquisitionKnowledge Analysis
相關次數:
  • 被引用被引用:14
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近年來,電腦輔助教學系統隨著網際網路的普及,愈來愈受重視,相關的應用技術及研究也相繼被提出,目的是希望讓電腦輔助教學系統可以如教師實際傳授課業給學生,導引學生進行學習;也希望利用電腦處理大量資訊及運算的能力,根據學生不同的學習狀況,提供適性化的教材,以補教師在傳統教學環境中,因學生人數眾多,無法因材施教的不足。在電腦輔助教學系統中,存在大量的教學內容與教學策略相關資訊,我們可統稱為電腦輔助教學系統中的知識。如何管理與存取大量的教材與教學策略,以讓教師與學生使用系統更為順利;如何擷取與儲存教師的教學經驗及策略,俾利於電腦輔助教學;如何分析學生的學習過程資訊,以藉由分析結果來更新原始教材與教學策略的設計;如何縮減系統管理所需的人力資源,以確實達到使用電腦系統的好處;以上這些問題可歸納成如何在電腦輔助教學系統中做好知識管理的工作。
在此論文中,我們提出電腦輔助教學專家系統(Computer-Assisted Instruction Expert System, CAI-ES),利用規則式專家系統的技術,推論儲存在知識庫中教師的教學策略與教學時的學習地圖,以依據學生的學習狀況,提供適當之教材予學生來進行學習;也就是說,每位學生的學習路徑與所學習的教材將依據本身的狀況而不同,以達到適性化教學的目標。
針對在電腦輔助教學專家系統中的知識管理,我們也提出相對應的知識管理機制,包括知識表達方法、知識擷取方法、知識管理器及知識分析器。在知識表達法中利用物件化特性,建構物件化教材與物件化規則集合,其中物件化規則集合即是儲存教師的教學策略,並搭配教學本體論的建構,可視為學習地圖,以達到適性化教學的目標。在知識擷取方法中,提出兩階段知識擷取,先建構學習地圖,再透過階層式表格法進行教學策略的知識擷取動作,最後建構出整體系統的教學知識庫。在知識管理器中,建構一個教材目錄管理器,透過如目錄服務的特性,讓教師及學生可以分享網路上的教材,這個教材目錄管理器並有半自動自我調整目錄結構的功能,以節省人力管理資源。最後,在知識分析器中,學生經過學習後,所留下的學習紀錄,可以透過所提出的兩階段模糊資料探勘及學習演算法進行分析後,提供教師及教材編輯者一些教材及學習地圖修改的建議資訊,以進行教學知識庫中相關知識的更新及修正動作。
With the fast growing and globally accepted of e-learning technology, Computer-Assisted Instruction (CAI) systems become a matter of great importance. The related applied technologies and studies are proposed to achieve the goal of guiding the learning process of students similar to teachers guide students to learn in traditional classroom. Moreover, based upon the ability of processing dramatic information, CAI system can provide different teaching materials for different students to learn according to the student’s learning aptitudes and records. In addition, there exist huge amount of teaching materials and teaching strategies in CAI system, called knowledge of CAI system. How to manage and access contents and teaching strategies of teachers for easily using e-learning system, how to extract teaching strategies from teachers for guiding students in learning process, how to analyze learning records of students for refining the design of original teaching materials and teaching strategies, and how to reduce manpower for maintaining e-learning system can be concluded as the problem, how to manage knowledge in e-learning system, and become an important issue.
In this thesis, we propose a Computer-Assisted Instruction Expert System (CAI-ES), which applies rule-based expert system technology to infer the teaching strategies and learning map of knowledge base, and then provide different teaching materials for student to learn according to different learning aptitudes and records. In other words, each student has the different learning path and different teaching materials according to the learning aptitudes to achieve the goal of learning individually.
For knowledge management in CAI-ES, we propose the corresponding knowledge management mechanisms, including Knowledge Representation, Knowledge Acquisition, Knowledge Organizer, and Knowledge Miner, to manage contents and knowledge. For Knowledge Representation, Object-Oriented Course Model (OOCM) and Object-Oriented Rule Model (OORM) are proposed to represent the teaching objects and the teaching strategies based upon object-oriented concepts. By applying the concept of ontology considered as learning map, the Teaching Strategy Knowledge Base (TS-KB) can be used to infer the suited teaching materials according to the learning aptitudes of students. For Knowledge Acquisition, Two-phase Knowledge Acquisition (Tp-KA) is proposed to acquire the learning map first and then to extract teaching strategies based upon Hierarchical Repertory Grid Analysis from teachers. For Knowledge Organizer, Course Directory Manager (CDM), which is a kind of web directory service, is proposed to manage and share Internet teaching resources. CDM can adjust the directory hierarchy semi-automatically to reduce the managing load. Finally, for Knowledge Miner, Two-phase Fuzzy Mining and Learning Algorithm (Tp-FML) is proposed to analyze the historical learning records of students and provide the suggestion about how to refine the teaching materials and learning map.
Abstract (In Chinese) i
Abstract .......................................................................................................................iii
Contents................. vi
List of Tables.......... ix
List of Figures......... x
List of Algorithms..... xiii
List of Examples...... xiv
Chapter 1 Introduction 1
Chapter 2 Background 7
2.1 Computer-Assisted Instruction (CAI) 7
2.2 Knowledge Management in CAI 9
Chapter 3 Overview of CAI-ES 11
3.1 Knowledge Management Model in CAI Domain 11
3.2 Architecture of CAI-ES 13
Chapter 4 Knowledge Representation 16
4.1 Object-Oriented Course Model (OOCM) 16
4.2 Representation of OOCM 20
4.3 Fundamental Course Construction and Revision 23
4.4 Expert System Approach of Constructing and Revising Course 30
4.4.1 Course Construction in CAI-ES 31
4.4.2 Query Transformer and Teaching Resource Database 32
4.5 Ontology Representation of TS-KB 37
4.6 Object-Oriented Rule Model (OORM) 39
Chapter 5 Knowledge Acquisition 41
5.1 Teaching Object Construction 42
5.2 Two-phase Knowledge Acquisition 44
5.2.1 Knowledge Construction in The First Phase 46
5.2.2 Knowledge Hierarchy Transformation and Knowledge Extraction in The Second Phase 54
5.3 Teaching Resource Hierarchy Construction 61
5.4 Summary 64
Chapter 6 Knowledge Organizer 66
6.1 Course Directory Manager (CDM) 69
6.2 XML-based Documents to Store The Internal Structure Information 71
6.2.1 Directory Structure (DS) Document 73
6.2.2 Directory Information (DI) Document 75
6.2.3 Course Information (CI) Document 77
6.3 The Associative Link 79
6.4 Algorithms of CDM 80
6.4.1 The Directory Locator Algorithm (DLAlg) 82
6.4.2 The Directory Splitter Algorithm (DSAlg) 83
6.4.3 The Associative Link Constructor Algorithm (ALCAlg) 86
6.5 Experiments 88
6.6 Summary 90
Chapter 7 Knowledge Miner 91
7.1 Architecture of Knowledge Miner in CAI-ES 92
7.2 The Flow of Two-phase Fuzzy Mining and Learning Algorithm 94
7.3 Look Ahead Fuzzy Mining Association Rule Algorithm 97
7.4 Inductive Learning Algorithm 103
7.5 Summary 107
Chapter 8 Implementation: CAI-ES 108
8.1 Implementation of Authoring Interface 108
8.2 Implementation of Tp-KA 114
8.3 Implementation of CDM 115
Chapter 9 Conclusion 116
Bibliography.......... 120
Appendix A (EMCUD) 130
Appendix B (DS Document Schema) 133
Appendix C (DI Document Schema) 134
Appendix D (CI Document Schema) 135
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[TT+01] C. J. Tsai, S. S. Tseng, C. T. Chen, H. L. Hsu, and J. R. Cheng, “Design of An Object-oriented Teaching Material Authoring System,” Proc. of GCCCE/ICCAI’01, pp. 1008-1015, Taipei, Taiwan, R.O.C., 2001.
[TT+01-1] C. J. Tsai, S. S. Tseng, and C. Y. Lin, “A Two-phase Fuzzy Mining and Learning Algorithm for Adaptive Learning Environment,” Proc. of Int’l Conf. on Computational Science, Lecture Notes in Computer Science (LNCS 2074), Vol. 2, pp. 429-438, CA, USA, May, 2001.
[TT02] C. J. Tsai and S. S. Tseng, “Building A CAL Expert System Based Upon Two-phase Knowledge Acquisition,” Expert Systems with Applications: An Int’l Jour., Vol. 22, No. 3, pp. 235-248, 2002.
[Vassileva97] J. Vassileva, “Dynamic Course Generation on The WWW,” Proc. of AI-ED''97, pp. 498-505, 1997.
[VO+97] F. J. Venter, G. D. Oosthuizen, and J. D. Roos, “Knowledge Discovery in Databases Using Lattices,” Expert Systems with Applications: An Int’l Jour., Vol. 13, No. 4, pp. 259-264, 1997.
[Winston92] P. H. Winston, Artificial Intelligence 3rd, Addison Wesley Publishing Company, 1992.
[W3C/RDF] Resource Description Framework (RDF), World Web Consortium (W3C), http://www.w3.org/TR/.
[W3C/XML] Extensible Markup Language (XML), World Web Consortium (W3C), http://www.w3.org/TR/.
[W3C/XSL] Extensible StyleSheet Language (XSL), World Web Consortium (W3C), http://www.w3.org/TR/.
[WB+94] J. Wan, M. Bieber, J. T. L. Wang, and P. A. Ng, “Document Management through Hypertext: A Logic Modeling Approach,” Proc. of Int''l Conf. on System Sciences, ICSS''27, Vol. 3, pp. 558 —568, 1994.
[WC00] X.Wu and K. Cai, “Knowledge Object Modeling,” IEEE Trans. on Systems, Man, and Cybernetics-Part A: Systems and Humans, Vol. 30, No. 2, pp. 96-107, 2000.
[WH+97] C. H. Wang, T. P. Hong, and S. S. Tseng, “Integrating Fuzzy Knowledge by Genetic Algorithms,” IEEE Trans. on Evolutionary Computation, Vol. 3, No. 4, 1997.
[WK84] D. J. Weiss and G. G. Kingsburg, “Application of Computerized Adaptive Testing to Educational Problems,” Jour. of Educational Measurement, Vol. 21, pp. 361-375, 1984.
[WT02] C. H. Wang, C. J. Tsai, T. P. Hong, and S. S. Tseng, “Fuzzy Inductive Learning Strategies,” appear to Jour. of Applied Intelligence.
[YL96] B. Yuwono and D. L. Lee, “Wise: A World Wide Web Resource Database System,” IEEE Trans. on Knowledge and Data Engineering, Vol. 8, No. 4, pp.548-554, 1996.
[YO+99] M. Yumoto, K. Oh, N. Komoda, and T. Mori, “Customization Rule Generation for Electronic Sales Promotion System in Wholesale Industry,” Proc. of Int’l Conf. on Advance Issues of E-Commerce and Web-Based Information Systems, pp. 50-55, 1999.
[YS99] P. T. Yu and C. W. Shyi, “Intelligent computer-Assisted Instruction system: A mental-model approach,” Project Report of National Science of ROC, NSC 85-2511-S-194-005, 1999.
[ZG+99] X. Zhu, S. Gauch, L. Gerhard, N. Kral, and A. Pretschner, “Ontology-based Web Site Mapping for Information Exploration,” Proc. of the 8th Int’l Conf. on Information and Knowledge Management, pp. 188-194, 1999.
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