Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/32632
DC FieldValueLanguage
dc.contributor.advisor李蔡彥<br>張俊彥zh_TW
dc.contributor.advisorLi , Tsai-Yen<br>Chang , Chun-Yenen_US
dc.contributor.author王浩全zh_TW
dc.contributor.authorWang , Hao-Chuanen_US
dc.creator王浩全zh_TW
dc.creatorWang , Hao-Chuanen_US
dc.date2003en_US
dc.date.accessioned2009-09-17T05:53:49Z-
dc.date.available2009-09-17T05:53:49Z-
dc.date.issued2009-09-17T05:53:49Z-
dc.identifierG0091753001en_US
dc.identifier.urihttps://nccur.lib.nccu.edu.tw/handle/140.119/32632-
dc.description碩士zh_TW
dc.description國立政治大學zh_TW
dc.description資訊科學學系zh_TW
dc.description91753001zh_TW
dc.description92zh_TW
dc.description.abstract本研究的目的在建構具備智慧型媒體特性之網路教學系統以增進網路學習的效果,特別是如何輔助學習者理解空間關係,以增進對空間幾何座標轉換的學習成效。電腦運算能力的增進使其成為極具潛力的教育媒體。基於教學及教育媒體的理論,本研究倡議在典型的網路教學中加入「智慧型媒體」的考量與設計以增進學習成效。智慧型媒體包括了兩個面向的考量,我們稱之為「媒體與方法」的考量 (Media and Method concern)。從媒體的角度來考量,電腦多媒體的使用應以能增進學習效果為原則,從「認知媒體」(Cognitive Media)的角度出發來設計網路教學的媒體呈現方式,媒體的目的在於清楚地傳達領域之知識給學習者。而從方法的角度來考量,應考慮如何運用電腦運算的特性以實現其他教育媒體裝置不能實現的教學策略及方法,例如互動式及適性化的教學。接續過去「智慧型教學系統」(Intelligent Tutoring System)及「適性化超媒體」(Adaptive Hypermedia) 的研究,本研究提出一套適性化的機制,將一般適性化系統中課程排序(adaptive course sequencing)的機制明確分離為「學習概念排序」及「教材選擇」兩個部分,達到更佳的抽象化及運用教學策略上的彈性。\n本研究以「空間座標轉換」做為領域知識,基於「媒體與方法」的考量,設計了稱為CooTutor (Coordinate Tutor)的網路教學系統來輔助空間座標轉換的學習。運用了電腦動畫技術,這個系統使用互動式三維媒體(Interactive 3D Media)清楚有效地傳達領域知識。由於空間座標轉換的學習相信與學習者的空間能力(Spatial Ability)相關,本研究透過實驗來探討互動式三維媒體的使用與空間能力增進之間的關係。另外,我們也研究並評估如何將空間能力及學習風格(learning styles)等學習者個人特質作為適性化依據,以及如何設計相對應的適性化機制。\n本研究的主要貢獻包括了 (1) 提出了使用智慧型媒體的概念,以「媒體與方法」的考量來討論網路教學的學習成效,及 (2) 提出一個創新及可行的架構將互動式三維媒體及適性化技術結合、運用於網路學習的學習模式上以輔助空間座標轉換的學習。zh_TW
dc.description.abstractThe objective of this research is on developing a Web-based educational system with intelligent media to enhance learners’ learning effects, especially to facili-tate learners’ spatial reasoning on learning spatial geometry topics. The increas-ing computing power allows us to use computers as powerful educational media. Based on theories of pedagogy and educational media, we propose to integrate intelligent media into typical Web-based learning paradigm to improve learning. “Intelligent media” in this research refers to two aspects of considerations. They are media—cognitive media aspect and method—intelligent tutoring aspects. The consideration of cognitive media aims at offering learners the most ease-of-understand presentation of a particular domain. The consideration of in-telligent tutoring targets to offer learners personalized learning experience based on individuals’ learning needs. To achieve better abstraction and flexibility in the adaptive mechanism, we have chosen to separate the concept sequencing from the underlying task of selecting appropriate learning materials.\nBy considering the characteristics of spatial geometry concepts, a Web-based learning environment called CooTutor (Coordinate Tutor) for learning spatial geometric transformation (SGT) is developed. Interactive 3D media is integrated into the system for delivering domain concepts effectively. Since the domain, spatial geometric transformation is evidently related to spatial ability (a group of human abilities about the use of space). This research attempts to address the relation between spatial ability and interactive 3D media via experimental evaluations. Moreover, learners’ latent traits, including spatial ability and learn-ing styles are considered to be used in adaptive material selection.\nThe main contribution of this research would be (1) the conceptualization of in-telligent media and the M&M concern for effective Web-based learning, and (2) an innovative approach and tenable architecture of employing 3D computer graphics and adaptive technologies in Web-based learning context for SGT learning.en_US
dc.description.tableofcontentsCHAPTER 1 Introduction 1\nCHAPTER 2 Literature Review 6\n2.1 Theoretical background of learning with media 6\n2.2 3D computer graphics in computer-based educational system 7\n2.3 Intelligent tutoring system and adaptive educational hypermedia 9\n2.3.1. Model- and procedure-based approaches for Web-based learning 10\n2.3.2. Methods and evaluation of methods in AH 11\n2.3.3. Difference between ITS and AEH 12\n2.4 Spatial ability 13\n2.5 Learning styles 14\nCHAPTER 3 CooTutor System 17\n3.1 System architecture 17\n3.2 User interface with Interactive 3D Media 18\n3.3 Adaptivity in CooTutor 20\n3.3.1 Domain modeling 21\n3.3.2 Student modeling 24\n3.3.3 Concept sequencing 29\n3.3.4 Material selection 30\n3.3.5 Client-side tuning 37\nCHAPTER 4 Evaluating the Effects of Media Representation 41\n4.1 Design of the experiment 41\n4.2 Learning materials with different representations 43\n4.3 Measuring instrument 44\n4.4 Data analysis 46\n4.5 Experimental results 47\n4.6 Discussion and implications 49\nCHAPTER 5 Evaluating the Effects of Adaptation based on Learners’ Traits 51\n5.1 Walkthrough on how CooTutor works 52\n5.2 Design of the experiment 57\n5.3 Measuring instrument 60\n5.4 Data analysis 61\n5.5 Experimental Results 62\n5.5.1 Result of spatial ability enhancement 64\n5.5.2 Result of SGT achievement 64\n5.5.3 Learners’ attitude on CooTutor 65\n5.5.4 Learners’ usage behavior 66\n5.6 Discussion 66\nCHAPTER 6 Conclusion 70\nREFERENCES 72\nAPPENDIX A 78\nAPPENDIX B 79zh_TW
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dc.source.urihttp://thesis.lib.nccu.edu.tw/record/#G0091753001en_US
dc.subject適性化超媒體zh_TW
dc.subject互動式三維媒體zh_TW
dc.subject智慧型教學系統zh_TW
dc.subject教育媒體zh_TW
dc.subject學習成效評估zh_TW
dc.subjectAdaptive Hypermediaen_US
dc.subjectInteractive 3D Mediaen_US
dc.subjectIntelligent Tutoring Systemsen_US
dc.subjectEducational Mediaen_US
dc.subjectEvaluation of Learning Achivementen_US
dc.titleA Web-based Tutoring System with Intelligent Media: Spatial Geometric Transformation as an Examplezh_TW
dc.title具備智慧型媒體特性之網路教學系統:以空間座標轉換為例zh_TW
dc.typethesisen
dc.relation.reference[1] ADL Initiative, SCORM Version 1.2 and 1.3, available at http://www.adlnet.org.zh_TW
dc.relation.reference[2] S. Ainsworth, Tutorial on Evaluation Methods for Learning Environments, in Interna-tional Conference on Artificial Intelligence in Education, 2003. http://www.cs.usyd.edu.au/~aied/Ainsworth_tutorial.pdfzh_TW
dc.relation.reference[3] M. Alias, T. R. Black, D. E. Gray, “Effect of Instruction on Spatial Visualisation Ability in Civil Engineering Students,” International Educational Journal, Vol. 3, No 1, 2002.zh_TW
dc.relation.reference[4] A. Aron and E. N. Aron, Statistics for Psychology, 2nd ed., Prentice-Hall, 2000.zh_TW
dc.relation.reference[5] D. P. Ausubel, J. S. Novak, and H. Hanesian, Educational Psychology: A Cognitive View, Holt, Rinehart & Winston: New York, 1978.zh_TW
dc.relation.reference[6] R. Baeze-Yates and B Ribeiro-Neto, Modern Information Retrieval, Addison Wesley, 1999.zh_TW
dc.relation.reference[7] D. Borsboom and G. J. Mellenbergh, “True scores, latent variables, and constructs: A comment on Schmidt and Hunter,” Intelligence, Vol. 30, pp. 505-514, 2002.zh_TW
dc.relation.reference[8] T. Branoff, P. E. Connolly, “The Addition of Coordinate Axes to the Purdue Spatial Visu-alization Test-Visualization of Rotations: A Study at Two Universities,” in Proc. of ASEE Annual Conference & Exposition, 1999.zh_TW
dc.relation.reference[9] G. M. Bodner and R. B. Guay, “The Purdue Visualization of Rotations Test,” The Chemical Educator, Vol. 2, No. 4, 1997.zh_TW
dc.relation.reference[10] P. Brusilovsky, “Methods and Techniques of Adaptive Hypermedia,” User Modeling and User-Adapted Interaction, 6 (2-3), pp. 87-129, 1996.zh_TW
dc.relation.reference[11] P. Brusilovsky, “Adaptive and Intelligent Technologies for Web-based Education,” K&uuml;n-stliche Intelligenz, 4, pp. 19-25, 1999.zh_TW
dc.relation.reference[12] P. Brusilovsky, “Course Sequencing for Static Courses? Applying ITS Techniques in Large-Scale Web-based Education,” Proc. of International Conference on Intelligent Tutoring Systems, pp. 625-634, 2000.zh_TW
dc.relation.reference[13] P. Brusilovsky and J. Vassileva, “Course Sequencing techniques for large-scale web-based education,” Int. J. Cont. Engineering Education and Lifelong Learning, 2003.zh_TW
dc.relation.reference[14] P. Brusilovsky, “Adaptive Hypermedia,” User Modeling and User-Adapted Interaction, 11, pp. 87-110, 2001.zh_TW
dc.relation.reference[15] B. Carr and I. P. Goldstein, “Overlays: a Theory of Modeling for Computer Aided In-struction,” AI Memo, 1977.zh_TW
dc.relation.reference[16] C-Y Chang, “The Impact of Different Forms of Multimedia CAI on Students’ Science Achievement,” Innovations in Education and Teaching International (IETI), Vol. 39, Is-sue 4, pp. 280-288, 2002.zh_TW
dc.relation.reference[17] D. N. Chin, “Empirical Evaluation of User Models and User-Adapted Systems,” User Modeling and User-Adapted Interaction, Vol. 11, pp. 181-194, 2001.zh_TW
dc.relation.reference[18] J. Cohen, Statistical Power Analysis for the Behavioral Sciences 2nd ed., NJ: Lawrence Erlbaum, 1988.zh_TW
dc.relation.reference[19] M.T.H. Chi, S. A. Siler, H. Jeong, T. Yamauchi, R. G. Hausmann, “Learning from Hu-man Tutoring,” Cognitive Science, 25, pp. 471-533, 2001.zh_TW
dc.relation.reference[20] R. E. Clark, “Media will Never Influence Learning,” Educational Technology Research and Development, 42(2), pp. 21-29, 1994.zh_TW
dc.relation.reference[21] A. T. Corbett and J. R. Anderson, “Locus of Feedback Control in Computer-Based Tu-toring: Impact on Learning Rate, Achievement and Attitudes,” in Proc. of ACM CHI’2001 Conference on Human Factors in Computing Systems, 245-252, 2001.zh_TW
dc.relation.reference[22] T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduciton to Algorithms, 2nd Ed., MIT Press, 2001.zh_TW
dc.relation.reference[23] L. G. Daniel, “Statistical significance testing: a historical overview of misuse and misinterpretation with implication for the editorial policies of educational journals,” Research in the Schools, Vol. 5, pp. 23-32, 1998.zh_TW
dc.relation.reference[24] P. De Bra, P. Brusilovsky, G. Houben, “Adaptive Hypermedia: From Systems to Frame-work,” ACM Computing Surveys, Vol. 31, No. 4es, Dec. 1999.zh_TW
dc.relation.reference[25] C. Dede, M. C. Salzman, R. B. Loftin, and D. Sprague, “Multisensory Immersion as a Modeling Environment for Learning Complex Scientific Concepts,” Computer Modeling and Simulation in Science Education, Springer-Verlag, 1999.zh_TW
dc.relation.reference[26] S. Draper, “Learning Styles notes,” Psychology Department, University of Glasgow, 2003zh_TW
dc.relation.referenceavailable at http://www.psy.gla.ac.uk/~steve/lstyles.htmlzh_TW
dc.relation.reference[27] Andrea A. diSessa, Changing minds: computer, learning, and literacy, The MIT Press, 2001.zh_TW
dc.relation.reference[28] J. Eklund and P. Brusilovsky, “The Value of Adaptivity in Hypermedia Learning Envi-ronments: A Short Reiview of Empirical Evidence,” in Proc. of 9th ACM International Hypertext Conference, Pittsburgh, PA, June 1998.zh_TW
dc.relation.reference[29] R. M. Felder and L. K. Silverman, “Learning and Teaching Styles in Engineering Educa-tion,” Engineering Education, 78(7), pp. 674-681, 1988.zh_TW
dc.relation.reference[30] R. M. Felder, G. N. Felder, and E. J. Dietz, “The Effects of Personality Type on Engi-neering Student Performance and Attitudes,” Journal of Engineering Education, 9(1), pp. 3-17, 2002.zh_TW
dc.relation.reference[31] J. E. Gilbert, R. Hubscher and S. Puntambekar Ed., Proc. of Workshop on Assessment Methods in Web-based Learning Environment and Adaptive Hypermedia, affiliated with International Conference on Artificial Intelligence in Education, 2001.zh_TW
dc.relation.referenceavailable at http://www.eng.auburn.edu/~gilbert/AIED2001/zh_TW
dc.relation.reference[32] D. N. M. de Gruijter and L. J. Th. van der Kamp, Statistical Test Theory for Education and Psychology, Graduate School of Education, Universiteit Leiden, Netherlands, 2003.zh_TW
dc.relation.referenceavailable at http://icloniis.iclon.leidenuniv.nl/gruijter/zh_TW
dc.relation.reference[33] J. Han and M. Kamber, Data mining: Concepts and Techniques, Academic Press, 2001.zh_TW
dc.relation.reference[34] Y. Hijikata, “Implicit User Profiling for On Demand Relevance Feedback,” in Proc. of ACM Intelligent User Interface Conference (IUI 2004), Portugal, January 2004.zh_TW
dc.relation.reference[35] D. E. Hinkle, W. Wiersma, and S. G. Jurs, Applied Statistics for the Behavioral Sciences, Houghton Mifflin, 1994.zh_TW
dc.relation.reference[36] B. Hokanson and S. Hooper, “Computers as cognitive media: examining the potential of computers in education,” Computers in Human Behavior, 16:537-552, 2000.zh_TW
dc.relation.reference[37] P. Holt, S. Dubs, M. Jones and J. Greer, “The State of Student Modeling,” Student Mod-elling: The Key to Individualized Knowledge-Based Instructution, pp. 3-35, Springer-Verlag, 1991.zh_TW
dc.relation.reference[38] R. Hubscher, “Logical Optimal Curriculum Sequences for Adaptive Hypermedia Sys-tems,” in Proc. of Adaptive Hypermedia, 2000.zh_TW
dc.relation.reference[39] B. E. Huitema, The Analysis of Covariance and Alternatives, John Wiley & Sons, New York, 1980.zh_TW
dc.relation.reference[40] Schuyler W. Huck, Reading Statistics and Research, Addison Wesley Longman, 2000.zh_TW
dc.relation.reference[41] T. Huk, M. Steinke, C. Floto, “The Influence of Visual Spatial Ability on the Attitude of Users towards High-Quality 3D-animations in Hypermedia Learning Environments,” in Proc. of E-Learn’03, 2003.zh_TW
dc.relation.reference[42] C. Hundhausen, S. Douglas, and J. Stasko, “A Meta-Study of Algorithm Visualization Effectiveness,” Journal of Visual Languages and Computing, Vol. 13, No. 3, pp. 259-290, June 2002.zh_TW
dc.relation.reference[43] W-Y. Hwang, C-B. Chang and G-J. Chen, “The Relationship of Learning Traits, Motiva-tion and Performance-Learning Response Dynamics,” Computers and Education, Vol. 42, pp. 267-287, 2004.zh_TW
dc.relation.reference[44] IMS Simple Sequencing Specification 1.0,zh_TW
dc.relation.referencehttp://www.imsglobal.org/simplesequencing/index.cfmzh_TW
dc.relation.reference[45] P. Ji, J. Kurose and B. Woolf, “Student Behavioral Model Based Prefetching in Online Tutoring System,” Technical Report, Department of Computer Science, University of Massachusetts at Amherst, 2001.zh_TW
dc.relation.reference[46] Judy Kay, “Stereotypes, Student Models and Scrutability,” in Proc. of Intelligent Tutor-ing Systems 2000 (ITS 2000), LNCS 1839, pp. 19-30, 2000.zh_TW
dc.relation.reference[47] P. K. Koehler, FastScript3D, A Companion to Java 3D, Jet Propulsion Laboratory, Cali-fornia Institute of Technology, 2002. available at http://fastscript3d.jpl.nasa.gov/zh_TW
dc.relation.reference[48] S. Lajoie and S. Derry, “A Middle Camp for (Un)Intelligent Instructional Computing: An Introduction,” Computers as Cognitive Tools, pp. 1-11, NJ: Erlbaum., 1993.zh_TW
dc.relation.reference[49] R. Kozma, \"Learning with media,\" Review of Educational Research, 61(2), pp. 179-212, 1991.zh_TW
dc.relation.reference[50] J. E. McLean and J. M. Ernest, “The role of statistical significance testing in educational research,” Research in the Schools, Vol. 5, pp. 15-22, 1998.zh_TW
dc.relation.reference[51] E. Melis, E. Andres, E. Budenbender, A. Frischauf, “ActiveMath: A Generic and Adap-tive Web-based Learning Environment,” International Journal of Artificial Intelligence in Education, 12:385-407, 2001.zh_TW
dc.relation.reference[52] J. Mohler, “Re-examining 3D Web Technologies for Education,” in Proc. of World Conference on the WWW and Internet, pp. 402-407, 2000zh_TW
dc.relation.reference[53] S. Olkun, “Making Connections: Improving Spatial Abilities with Engineering Drawing Activities,” International Journal of Mathematics Teaching and Learning, April, 2003.zh_TW
dc.relation.reference[54] R. Parent, Computer Animation: Algorithms and Techniques, Academic Press, 2002.zh_TW
dc.relation.reference[55] B. R. Preiss, Data Structures and Algorithms with Object-Oriented Design Patterns in C++, John Wiley & Sons, 1999.zh_TW
dc.relation.reference[56] M. Recker and A. Ram, “Cognitive Media Types as Indices for Hypermedia Learning Environments,” in Proc. of the AAAI-94 Workshop on Indexing and Reuse in Multimedia Systems, Seattle, WA, 1994.zh_TW
dc.relation.reference[57] E. Rich, “User Modeling via Stereotypes,” Cognitive Sciences, Vol. 3, pp.355-366, 1979.zh_TW
dc.relation.reference[58] J. Rieman, M. Franzke and D. Redmiles, “Usability Evaluation with the Cognitive Walk-through,” in Proc. of ACM Annual Conference on CHI (CHI’95), 1995.zh_TW
dc.relation.reference[59] J.A. Self. “Bypassing the intractable problem of student modelling,” Intelligent Tutoring Systems: at the Crossroads of Artificial Intelligence and Education, pages 107--123, Norwood, NJ, 1990.zh_TW
dc.relation.reference[60] V. J. Shute, “A Comparison of Learning Environments: All That Glitters…”Computers as Cognitive Tools, pp. 47-73, NJ: Erlbaum., 1993.zh_TW
dc.relation.reference[61] B. A. Soloman and R. M. Felder, Index of Learning Styles Questionnaire, available at http://www.engr.ncsu.edu/learningstyles/ilsweb.htmlzh_TW
dc.relation.reference[62] N. Stach, A. Cristea and P. De Bra, “Authoring of Learning Styles in Adaptive Hyperme-dia,” in Proc. of WWW Conference, NY, USA, 2004.zh_TW
dc.relation.reference[63] M. K. Stern and B. P. Woolf, “Adaptive Content in an Online Lecture System,” in Proc. of Adaptive Hypermedia 2000, LNCS 1892, pp. 227-238, 2000.zh_TW
dc.relation.reference[64] J. Stevens, Applied Multivariate Statistics for the Social Science 3rd ed., NJ: Lawrence Erlbaum, 1996.zh_TW
dc.relation.reference[65] A. Strehl, Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining, Doctorial Dissertation, The University of Texas at Austin, May 2002.zh_TW
dc.relation.reference[66] E. Triantafillou, A. Pomportsis, S. Demetriadis and E. Georgiadou, “The Value of Adap-tivity based on Cognitive Style: An Empirical Study,” British Journal of Educational Technology, Vol. 35, No. 1, pp. 95-106, 2004.zh_TW
dc.relation.reference[67] A. Tretiakov, Kinshuk, T. Tretiakov, “Designing Multimedia Support for Situated Learn-ing,” Proc. of IEEE International Conference on Advanced Learning Technologies, 2003.zh_TW
dc.relation.reference[68] V. Tsiriga and M. Virvou, “Initializing the Student Model using Stereotypes and Machine Learning,” in Proc. of IEEE International Conference on Systems, Man and Cybernetics, 2002.zh_TW
dc.relation.reference[69] NASA Johnson Space Center, The Virtual Astronaut Website Project, available at http://virtualastronaut.jsc.nasa.gov.zh_TW
dc.relation.reference[70] NASA Science@NASA website, “Whatever happened to Virtual Reality,” available at http://science.nasa.gov/headlines/y2004/21jun_vr.htmzh_TW
dc.relation.reference[71] J. Rickel and W. L. Johnson, “Animated Agents for Procedural Training in Virtual Real-ity: Perception, Cognition, and Motor Control,” Applied Artificial Intelligence, 13:343-382, 1999.zh_TW
dc.relation.reference[72] F. L. Schmidt and J. E. Hunter, “Theory Testing and Measurement Error,” Intelligence, 27(3), pp.183-198. 1999.zh_TW
dc.relation.reference[73] B. J. Underwood and J. J. Shaughnessy, Experimentation in Psychology, John Wiley&Sons, New York, 1975.zh_TW
dc.relation.reference[74] J. Vassileva, “Instructional Planning Approaches: from Tutoring towards Free Learning,” in Proc. of Euro-AIED, 1996.zh_TW
dc.relation.reference[75] H-C. Wang, T-Y. Li, \"Considering Model-based Adaptivity for Learning Objects,\" Learning Technology newsletter, Vol. 6, Issue 2, April 2004.zh_TW
dc.relation.reference[76] R. Wolfe, “A Syllabus Survey: Examining the State of Current Practice in Introductory Computer Graphics Courses,” ACM SIGGRAPH Computer Graphics, Volume 33, Issue 1, 1999.zh_TW
dc.relation.reference[77] B. Woolf, M. Romoser, D. Bergeron, D. Fisher, “Tutoring 3-Dimensional Visual Skills: Dynamic Adaptation to Cognitive Skill,” in Proc. of Artificial Intelligence in Education, 2003.zh_TW
dc.relation.reference[78] C. H. Yu, B. Onlund, S. DiGangi and A. Jannasch-Pennell, “Estimating the Reliability of Self-reported Data for Web-based Instruction,” AECT’s Annual International Convention, Long Beach, California, 2000.zh_TW
dc.relation.reference[79] B. Zayas, “Learning from 3D VR representations: learners-centered design, realism and interactivity,” in Proc. of Workshop on External Representations in AIED, May 2001.zh_TW
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