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姓名 周志岳(Chih-Yueh Chou)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 一個適用於解題領域的模擬多重學習同伴之方法
(An Approach to Modeling Multiple Learning Companions in Problem Solving Activities)
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摘要(中) 學習同伴系統 (Learning Companion System) 是一種電腦輔助學習系統,其中電腦藉由虛擬一個或多個的擬人化成員來提供學生一個社會學習的環境。其中由系統所虛擬的非權威、非專家的擬人化成員稱之為「學習同伴」。這些「學習同伴」可以成為學生的競爭者、指導者、被指導者或是合作者。有兩種方法可以來製作「學習同伴」的領域以及學習能力:機器學習(machine learning)與模擬(simulation)。採用模擬方法的優點是比較容易製作多個的「學習同伴」,也比較容易控制這些「學習同伴」來符合學習理論或是個別學生的需求。再者「量化模擬」(quantitative simulation)適合於製作學習能力中的「技能熟練」(skill refinement)。然而目前尚未有相關研究是採用「量化模擬」來製作一個扮演被指導者的「學習同伴」。另一方面有一些學者採用「量化模擬」來製作一個扮演競爭者的「學習同伴」,然而這些「學習同伴」只擁有整體性的熟練度,而欠缺個別詳細的技能熟練度。
本研究的目的是要提出一個採用「量化模擬」的「學習同伴」製作方法。這個方法必須適用於解題領域(problem solving),支援製作多個能夠扮演被指導者和競爭者、且具備個別詳細技能熟練度的「學習同伴」。解題領域是一個「學習同伴」需要運用「技能熟練」學習能力的領域。本論文所提出的方法稱之為QSLCM(Quantitative-Simulation based Learning Companion Modeling),此論文介紹了QSLCM方法的架構、步驟、解題模型以及扮演被指導者、競爭者和指導者的模擬機制和規則。QSLCM的架構可以視為傳統「智慧型導學系統」(Intelligent Tutoring System)架構的擴充。此架構同時也提供了一個途徑來比較「學習同伴系統」與「智慧型導學系統」。QSLCM採用兩個「學生模型製作」(user modeling)的技術:「覆蓋模型」(overlay model)與「錯誤模型」(bug model)。「學生模型製作」是觀察學生的行為來建立學生模型。而QSLCM則是「學生模型製作」的相反機制,也就是建立一個代表「學習同伴」的「學習同伴模型」(learning companion pattern)用來虛擬「學習同伴」的行為。採用「覆蓋模型」,「學習同伴」的可能解法是專家的子集合。而採用「錯誤模型」可以讓「學習同伴」犯錯。QSLCM採用機率來表現「學習同伴」技能熟練度與解題的不確定性。QSLCM藉由多個「學習同伴模型」來支援製作多個「學習同伴」。藉由修改「學習同伴模型」就可以改變該「學習同伴」的解題行為。
本論文也介紹了兩個採用QSLCM來製作的「學習同伴系統」。DwestAgent系統展現QSLCM支援製作多個具備不同程度與扮演不同角色「學生同伴」的能力。DwestAgent提供三個分別具備專家、中等生與新手程度的「學生同伴」。每一個「學生同伴」都可以扮演競爭者、被指導者與指導者。RTS系統是一個支援「教學相長」的「學習同伴系統」。RTS提供一個輪流扮演指導者與被指導者的「學習同伴」。
摘要(英) LCS (Learning Companion System), a type of computer assisted learning systems, simulates one or more artificial human-like agents to provide users with a social learning environment. The non-authoritative or non-expert agents of LCSs, called learning companions, can act as the competitor, tutor, tutee, or collaborator of the user. Two approaches exist for implementing the knowledge and learning ability of a learning companion: machine learning and simulation. The advantages of the simulation approach are that implementing multiple learning companions, and tailoring the learning companion to the requirements of learning theory and individual users is more convenient. Furthermore, quantitative simulation is suitable for supporting refinement of learning skills. However, applying quantitative simulation to modeling a learning companion as the tutee of a user remains little researched. On the other hand, some researchers have applied quantitative simulation to modeling a learning companion as a user’s competitor in their LCSs. However, these systems only provide a learning companion with an overall proficiency. The learning companion does not have special proficiencies in different skills.
This thesis aims to propose a quantitative simulation approach to modeling multiple learning companions in the domain of problem solving, particularly, modeling learning companions as tutees or competitors of users, and with specialized skill proficiencies. Problem solving is a domain, in which learning companions require implementation of skill refinement. The approach, called QSLCM (Quantitative-Simulation based Learning Companion Modeling), involves architecture, steps, problem solving modeling, and simulation of the roles of competitor, tutee, or tutor. Several heuristic rules for the approach are also proposed herein. QSLCM architecture is a generalization of the typical Intelligent Tutoring System (ITS) architecture. This architectural view provides a means of comparing the basic differences between ITSs and LCSs. QSLCM enables modeling learning companions by applying two techniques of user modeling, the overlay model and the bug model. User modeling aims to base the construction of the user’s model on observation of the user’s behavior. QSLCM is a reverse process of user modeling; namely, constructing a learning companion pattern to simulate the behavior of a learning companion. Applying the overlay model, the learning companion’s solutions to problems are a subset of the solutions of an expert. Meanwhile, applying the bug model allows the learning companion to make mistakes. In QSLCM, probabilities represent the uncertain skill proficiency behavior of the learning companion. Meanwhile, QSLCM supports modeling different kinds of learning companions by setting different learning companion patterns. Furthermore, QSLCM makes it convenient to change the behavior of learning companions simply by adjusting the learning companion pattern.
Two applications are developed by applying QSLCM to simulate learning companions. The first application, called DwestAgent, presents that QSLCM can support modeling multiple learning companions with different roles and different skill levels. DwestAgent has three learning companions, with skill levels of an expert, average student, and novice, respectively. Each learning companion can act as competitor, peer tutor, or tutee. The second application, called RTS, supports the learning activity of reciprocal tutoring. RTS provides users with a learning companion, which can act as tutee and peer tutor.
關鍵字(中) ★ 社會學習
★ 電腦輔助學習
★ 被指導者
★ 量化模擬
★ 學習同伴系統
★ 學習同伴
★ 教育代理人
關鍵字(英) ★ computer assisted learning
★ tutee
★ quantitative simulation
★ learning companion system
★ learning companion
★ social learning
★ educational agent
論文目次 Cover
Catalog
致謝
摘要
ABSTRACT
1. INTRODUCTION
1.1 LEARNING COMPANION SYSTEMS
1.2 MOTIVATION AND GOALS
1.3 BACKGROUND
1.4 RELATED WORKS
1.5 ORGANIZATION
2. A QUANTITATIVE SIMULATION APPROACH TO MODELING MULTIPLE LEARNING COMPANIONS IN THE DOMAIN OF PROBLEM SOLVING
2.1 ARCHITECTURE
2.2 STEPS AND OBJECTIVES
2.3 PROBLEM SOLVING MODELING(DOMAIN MODULE)
2.4 SIMULATION OF A TUTEE(BEHAVIOR MODULE)
2.5 SIMULATION OF A PEER TUTOR(BEHAVIOR MODULE)
3. FIRST APPLICATION:DWESTAGENT
3.1 INTRODUCTION
3.2 DWESTAGENT AND LEARNING MODELS
3.3 IMPLEMENTATION OF LEARNING COMPANIONS
3.4 SUMMARY
4. SECOND APPLICATION: RECIPROCAL TUTORING SYSTEM
4.1 EXPLORING DESIGN OF COMPUTER SUPPORTS FOR RECIPROCAL TUTORING
4.2 IMPLEMENTATION OF THE LEARNING COMPANION IN RTS
5. CONCLUSION
5.1 CONTRIBUTION
5.2 DISCUSSION AND FUTURE WORK
REFERENCES
參考文獻 Aimeur, E. & Frasson, C. (1996). Analyzing a New Learning Strategy According to Different Knowledge Levels. Computer and Education, Vol. 27, No. 2, pp. 115-127.
Aimeur, E., Dufort, H., Leibu, D., & Frasson, C. (1997). Some Justifications for the Learning by Disturbing Strategy. AI-ED 97 World Conference on Artificial Intelligence in Education, Kobe , Japan, pp. 119-126.
Anderson, J. R., Boyle, C. F., Corbett, A. T., & Lewis, M. W. (1990). Cognitive modeling and intelligent tutoring. Artificial Intelligence, 42, 7-49.
Aronson, E. (1978). The jigsaw classroom. Beverly Hills, CA: Sage.
Bhuiyan, S., Greer, J.E., & McCalla, G.I. (1992). Learning recursion through the use of a mental model-based programming environment, The 2nd International Conference of Intelligent Tutoring Systems, Lecture Notes in Computer Science, 608, Springer-Verlag, 50-57.
Blandford, A.E., (1994). Teaching through collaborative problem solving, Journal of Artificial Intelligence in Education, Vol. 5. No.1, 51-84.
Brown, A.L., (1992). Design experiments: theoretical and methodological challenges in creating complex interventions in classroom settings, The Journal of the Learning Sciences, 2(2), 141-178.
Brown, A.L., Ash, D., Rutherford, M., Nakagama, K., Gordon, A., & Campione, J.C. (1993). Distributed expertise in the classroom. In G. Salomon (ed.), Distributed Cognitions: Psychological and Educational Considerations, 188-288, New York: Cambridge University Press.
Brown, J.S. & Burton, R.R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, Vol. 2, pp.155-191.
Bull, S., Pain, H. & Brna, P. (1993). Student modeling in intelligent computer assisted language learning system: The issues of language transfer and learning strategies, Proceedings of the International Conference on Computers in Education, Taiwan, 121-126.
Burton, R.R., & Brown, J.S., (1979). An investigation of computer coaching for informal learning activities, International journal of Man-Machine Studies, Vol. 11, pp. 5-24.
Carr, B. & Goldstein, I. P. (1977). Overlays. A theory of modeling for computer-aided instruction, AI Lab Meno 406, MIT, Cambridge, Massachusetts.
Carbonell, J. R. (1970). AI in CAI: an artificial-intelligence approach to computer-assisted instruction. IEEE Transactions on Man-Machine Systems, 11(4), pp. 190-202.
Chan, T.W. (1989). Learning Companion Systems, Ph.D. Thesis, Computer Science Department, University of Illinois at Urbana-Champaign.
Chan, T.W. (1991). Integration-kid: a learning companion system. The Proceedings of the 12th International Joint Conference on Artificial Intelligence, Sydney, Australia, Morgan Kaufmann Publishers, Inc., 1094-1099.
Chan, T.W. (1995). A Tutorial on Social Learning Systems, in Emerging Technologies in Education, T.W. Chan & J. Self (eds), AACE.
Chan, T.W. & Baskin, A.B. (1988). Studying with the prince: The Computer as a Learning Companion. The Proceedings of International Conference of Intelligent Tutoring Systems, 1988, June, Montreal, Canada, 194-200.
Chan, T.W. & Baskin, A.B. (1990). Learning companion systems. In C. Frasson & G. Gauthier (Eds.) Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education, Chapter 1, New Jersey: Ablex Publishing Corporation.
Chan, T.W., Chung, Y.L., Ho, R.G., Hou, W.J. & Lin, G.L. (1992). Distributed learning companion systems -- WEST revisited. The 2nd International Conference of Intelligent Tutoring Systems, C. Frasson, G. Gauthier & G. McCalla (Eds.). Lecture Notes in Computer Science, 608, Springer-Verlag, 643-650.
Chan, T.W. & Chou, C.Y. (1995). Simulating a Learning Companion in Reciprocal Tutoring System. The First International Conference on Computer Support for Collaborative Learning (CSCL'95), Bloomington, Indiana, USA, pp. 49-56.
Chan, T.W. & Chou, C.Y. (1997). Exploring the Design of Computer Supports for Reciprocal Tutoring, International Journal of Artificial Intelligence in Education, Vol. 8, 1997, pp. 1-29.
Chan, T.W. & Lai, J.A., (1995). Contest-Kids: A Competitive Distributed Social Learning Environment, Proceedings of World Conference on Computers in Education, Birmingham, England, 767-776.
Chang, L.J., Wang, J.C., Hsu, B.Y., & Chan, T.W. (2000). Four Applications of Student Modeling — My Animal Companions, Proceedings of The Third Global Chinese Conference on Computers in Education, Macau, 366-370.
Chou, C.Y., Lin, C.J., & Chan, T.W. (1999). User modeling in simulating learning companions, 9th International Conference on artificial intelligence in education AI-ED 99, July, Le Mans, France, pp. 277-284.
Collins, A., Brown, J.S., & Newman, S. E. (1989). Cognitive apprenticeship: teaching the craft of reading, writing and mathematics. In L. B. Resnick (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser, Hillsdale, NJ: Lawrence Erlbaum Associates Publishers.
Collins, A. & Stevens, A. (1982). Goals and strategies of inquiry teachers. In R. Glaser (ed.), Advances in Instructional Psychology (Vol. 2, pp.65-119). Hillsdale, NJ: Erlbaum.
Dillenbourg, P & Self, J. (1992). People power: a human-computer collaborative learning system. C. Frasson, G. Gauthier & G. McCalla (Eds.). The 2nd International Conference of Intelligent Tutoring Systems, Lecture Notes in Computer Science, 608, Springer-Verlag, 651-660.
Gilmore, & Self, J. (1988). The application of machine learning to intelligent tutoring systems, In J. Self (ed.), Artificial Intelligence and human learning, intelligent computer-aided instruction, New York: Chapman and Hall, pp. 179-196.
Goodman, B., Soller, A., Linton, F., & Gaimari, R. (1998). Encouraging Student Reflection and Articulation using a Learning Companion. International Journal of Artificial Intelligence in Education, 9, pp. 237-255.
Hietala, P. & Niemirepo, T. (1998). The Competence of Learning Companion Agents. International Journal of Artificial Intelligence in Education, 9, 178-192.
Jehng, J.C., Shih, Y.F., Liang, S. & Chan, T.W. (1994). TurtleGraph: a computer supported cooperative learning environment, The Proceedings of the World Conference on Educational Multimedia and Hypermedia, Vancouver, Canada, AACE, 293-298.
Luehrmann, A. (1972). Should the computer teach the student or vice-versa? AFIPS 1972 Spring Joint Computer Conference Proceedings, Vol. 40, AFIPS, Montvale, N.J.; Also appeared in The Computer in the School: Tutor, Tool, Tutee, Taylaor, R.P. (ed), 1980, 129-135, Teacher College Press.
McCalla, G. (1990). The central importance of student modeling to intelligent tutoring, Research Report, 90-7, ARIES Laboratory, Computational Science Department, University of Saskatchewan, Saskatoon, Canada.
McManus, M.M., & Aiken, R.M. (1993). The group leader paradigm in an intelligent collaborative learning system. In S. Ohlsson, P. Brna, and H. Pain (Eds.), Proceedings of the World Conference on Artificial Intelligence in Education. Charlottesville, VA: Association for the Advancement of Computing in Education, 249-256.
Michalski, R. S., Carbonell, J. G. & Mitchell, T. M. (1983). Machine Learning: An Artificial Intelligence Approach.
Newell, & Simon, H. A., (1963). GPS, A Program That Simulates human Thought, Computers and Thought, E. A. Feigenbaum and J. Feldman (eds), McGraw-Hill, New York, pp. 279-293.
Nichols, D. (1994). Issues in designing learning by teaching systems, In Proceedings of the East-West International Conference on Computer Technologies in Education (EW-ED’94), 176-181.
Palincsar, A.S. & Brown, A.L. (1984). Reciprocal teaching of comprehension-fostering and monitoring activities. Cognition and Instruction, 1, 117-175.
Palthepu, S., Greer, J., & McCalla, G. (1991). Learning by teaching. The Proceedings of the International Conference on the Learning Sciences, AACE, 357-363.
Ragnemalm, E.L. (1996). Collaborative Dialogue with a Learning Companion as a Source of Information on Student Reasoning. In Proceedings of ITS'96 Conference, Springer-Verlag, 650-658.
Ramírez Uresti, J.A. (1999). LECOBA: A LEarning COmpanion system for binary Boolean Algebra. In Johnson, L. (Ed.), Proceedings of Workshop 1: Animated and Personified Pedagogical Agents. AI-ED'99 conference, pp. 56-61. Le Mans, France. In IJAIED Vol. 10, pp.1060-1069 .
Scardamalia, M. & Bereiter, C. (1991). Higher levels of agency for children in knowledge building: A challenge for the deign of new knowledge media. Journal of Learning Sciences, 1, 37-68.
Scott, L. A. & Reif, F. (1999). Teaching Scientific Thinking Skills: Students and Computers Coaching Each Other. The 9th International Conference on Artificial Intelligence in Education (AI-ED 99), Le Mans, France, pp. 285-293.
Self, J. (1974). Student models in computer-aided instruction, International Journal of Man-Machine Studies, 6, 261-276.
Self, J. (1985). A perspective on intelligent computer-assisted learning. Journal of Computer Assisted Learning, Vol. 1, 159-166.
Self, J. (1988). Bypassing the intractable problem of student modeling. International Conference of Intelligent Tutoring Systems, Montreal, Canada, 18-24.
Self, J. (1995). An Introduction to Artificial Intelligence in Education. In T.W. Chan & J. Self (Eds.) Emerging Technologies in Education, Charlottesville, VA: AACE. pp. 3-20.
Sleeman, D. & Brown, J. (1982). Intelligent Tutoring Systems. Academic Press.
Smith, D.C., Cypher, A., & Spohrer (1994). KIDSIM: Programming Agents Without a Programming Language, Communication of ACM, July, Vol 37, No. 7, 55-67.
Soldato, & du Boulay, B. (1995). Implementation of Motivational Tactics in Tutoring Systems, Journal of Artificial Intelligence in Education, Vol. 6 No. 4, pp. 337-378.
Taylor, R.P. (1980).The Computer in the School: Tutor, Tool, Tutee (ed), Teacher College Press.
Ur, S. & VanLehn, K. (1995). STEPS: A Simulated , Tutorable Physics Student. Journal of Artificial Intelligence in Education, 6(4), 405-435.
VanLehn, K. (1993). Keynote speech, World Conference on Artificial Intelligence in Education, Edinburgh, Scotland, August.
VanLehn, K., Ohlsson, S. & Nason, R. (1994). Applications of simulated students: an exploration, Journal of Artificial Intelligence in Education, Vol. 5 No. 2, 135-175.
Wang, W.C. (1992). Design of coherent pedagogical programming languages. Master thesis, Institute of Computer Science and Electronic Engineering, National Central University, Taiwan
Wenger, E. (1987), Artificial Intelligence and Tutoring Systems. Los Altos, CA: Morgan Kaufmann.
指導教授 陳德懷(Tak-Wai Chan) 審核日期 2000-7-4
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