學術產出-Theses
Article View/Open
- pdf(817)pdf(930)pdf(809)pdf(769)pdf(751)pdf(823)pdf(957)pdf(1787)pdf(1132)pdf(1064)pdf(856)pdf(1049)pdf(818)pdf(1004)
Publication Export
-
題名 利用貝氏網路建構綜合觀念學習模型之初步研究
An Exploration of Applying Bayesian Networks for Mapping the Learning Processes of Composite Concepts作者 王鈺婷
Wang, Yu-Ting貢獻者 劉昭麟
Liu, Chao-Lin
王鈺婷
Wang, Yu-Ting關鍵詞 智慧型教學系統
貝氏網路
潛在變項分析
教育評量
學生模型
Intelligent Tutoring System
Bayesian Networks
Latent Variable Analysis
Educational Assessment
Student Modeling
Automated Cognitive Diagnosis日期 2004 上傳時間 17-Sep-2009 14:08:46 (UTC+8) 摘要 本研究以貝氏網路作為表示教學領域中各個學習觀念的關係的語言。教學領域中的學習觀念包含了基本觀念與綜合觀念，綜合觀念是由兩個以上的基本觀念所衍生出來的觀念，而綜合觀念的學習歷程即為學生在學習的過程中如何整合這些基本觀念的過程。了解綜合觀念的學習歷程可以幫助教師及出題者了解學生的學習路徑，並修改其教學或出題的方針，以期能提供適性化的教學及測驗。為了從考生答題資料中尋找出這個隱藏的綜合觀念學習歷程，我們提出一套以mutual information以及一套以chi-square test所發展出來的研究方法，希望能夠藉由一個模擬環境中模擬考生的答題資料來猜測考生學習綜合觀念的學習歷程。初步的實驗結果顯示出，在一些特殊的條件假設下，我們的方法有不錯的機會找到暗藏在模擬系統中的學習歷程。因此我們進而嘗試提出一個策略來尋找較大規模結構中的學習歷程，利用搜尋的概念嘗試是否能較有效率的尋找出學生對於綜合觀念學習歷程。雖然在實驗中並沒有十分理想的結果，但是在實驗的過程中，我們除了發現學生答題資料的模糊程度為系統的正確率的主要挑戰之外，另外也發現了學生類別與觀念能力之間的關係也是影響實驗結果的主要因素。透過我們的方法，雖然不能完美的找出學生對於任何綜合觀念的綜合歷程，但是我們的實驗過程與結果也對隱藏的真實歷程結構提供了不少線索。最後，我們探討如何藉由觀察學生接受測驗的結果來分類不同學習程度與狀況的學生之相關問題與技術。我們利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，探討是否能透過學生的答題資料有效的分辨學生能力的類別。實驗結果顯示出，在每個觀念擁有多道測驗試題的情況下，利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，藉由考生答題資料來進行學生能力類別的分類可以得到不錯的正確率。我們希望這些探討和結果能對適性化教學作出一些貢獻。
In this thesis, I employ Bayesian networks to represent relations between concepts in pedagogical domains. We consider basic concepts, and composite concepts that are integrated from the basic ones. The learning processes of composite concepts are the ways how students integrate the basic concepts to form the composite concepts. Information about the learning processes can help teachers know the learning paths of students and revise their teaching methods so that teachers can provide adaptive course contents and assessments. In order to find out the latent learning processes based on students’ item response patterns, I propose two methods: a mutual information-based approach and a chi-square test-stimulated heuristics, and examine the ideas in a simulated environment.Results of some preliminary experiments showed that the proposed methods offered satisfactory performance under some particular conditions. Hence, I went a step further to propose a search method that tried to find out the learning process of larger structures in a more efficient way. Although the experimental results for the search method were not very satisfactory, we would find that both the uncertainty included by the students’ item response patterns and the relations between student groups and concepts substantially influenced the performance achieved by the proposed methods. Although the proposed methods did not find out the learning processes perfectly, the experimental processes and results indeed had the potential to provide information about the latent learning processes.Finally, I attempted to classify students’ competence according to their item response patterns. I used the nearest neighbor algorithm, the k-means algorithm, and some variations of these two algorithms to classify students’ competence patterns. Experimental results showed that the more the test items used in the assessment, the higher the accuracy of classification we could obtain. I hope that these experimental results can make contributions towards adaptive learning.參考文獻 Abramowitz, M. and Stegun, I. A. (Eds.), Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, ninth printing, New York: Dover, 824−825, 1972.
Aurenhammer, F., Voronoi diagrams - a survey of a fundamental geometric data structure, ACM Computing Surveys, 23, 345−405, 1991.
Birenbaum, M., Kelly, A. E., Tatsuoka, K. K., and Gutvirtz, Y., Attribute mastery patterns from rule space as the basis for student models in Algebra, International Journal of Human-Computer Studies, 40(3), 497–508, 1994.
Brian, T. L., K-Means Clustering, http://fconyx.ncifcrf.gov/~lukeb/kmeans.html, 2005.
Brusilovsky, P., Schwarz, E., and Weber, G., ELM-ART: An intelligent tutoring system on world wide web, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 261−269, 1996.
Bunt, A. and Conati, C., Assessing effective exploration in open learning environments using Bayesian networks, Proceedings of the Sixth International Conference on Intelligent Tutoring Systems, 698−707, 2002.
Bunt, A. and Conati, C., Probabilistic student modeling to improve exploratory behavior, User Modeling and User-Adapted Interaction, 13(3), 269−309, 2003.
Burns, H. L. and Capps, C. G., Foundations of Intelligent Tutoring Systems: An Introduction, Lawrence Erlbaum Associates, Hillsdale, NJ, 1988.
Chen, C.-M., Lee, H.-M., and Chen, Y.-H., Personalized e-leaning system using item response theory, Computer & Education, 44(3), 237−255, 2005.
Collins, J. A., Greer, J. E., and Huang, S. X., Adaptive assessment using granularity hierarchies and Bayesian nets, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 569–577, 1996.
Conati, C., Gertner, A. S., VanLehn, K., and Druzdzel, M. J., On-line student modeling for coached problem solving using Bayesian networks, Proceedings of the Sixth International Conference on User Modeling, 231−242, 1997.
Conati, C., Gertner, A. S., and VanLehn, K., Using Bayesian networks to manage uncertainty in student modeling, User Modeling and User-Adapted Interaction, 12, 371–417, 2002.
Cover, T. and Hart, P., Nearest neighbor pattern classification, Institute of Electrical and Electronics Engineers, Transactions on Information Theory, 13, 21−27, 1967.
Cover, T. M. and Thomas, J. A., Elements of Information Theory, John-Wiley and Sons, 1991.
Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J., Probabilistic Networks and Expert Systems, Springer-Verlag, New York, 1999.
Garthwaite, P. H., Jolliffe, I. T., and Jones, B., Statistical Inference, Prentice Hall, 1995.
Hambleton, R. K., Swaminathan, H., and Rogers, H. J., Fundamentals of Item Response Theory, Sage Publications, 1991.
Hatzilygeroudis, I., and Prentzas, J., Knowledge representation requirements for intelligent tutoring systems, Proceedings of the Seventh International Conference on Intelligent Tutoring Systems, 87−97, 2004.
Heckerman, D. and Breese, J. S., Causal independence for probability assessment and inference using bayesian networks, Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics, 26(6), 826−831,1994.
Heckerman, D., Learning Bayesian networks, Technical Report MSR-TR-95-02, Microsoft Research, 1995a.
Heckerman, D., A tutorial on learning with Bayesian networks, Technical Report MSR-TR-95-06, Microsoft Research, 1995b.
Heckerman, D., Mamdani, A., and Wellman, M. P., Real world applications of Bayesian networks, Communications of the ACM, 38,1995.
Hsu, C.-N., Chung, H.-H, and Huang, H.-S., Mining skewed and sparse transaction data for personalize shopping recommendation, Machine Learning, 57(1-2), 35–59, 2004.
Hugin Expert A/S., HUGIN API Reference Manual Version 6.2, http://developer.hugin.com/documentation/API_Manuals, 2004
Jensen, F. V., Bayesian Networks and Decision Graphs, Springer, 2001.
Liu, C.-L., Wang, Y.-T., and Liu, Y.-C., A Bayesian network-based simulation environment for investigating assessment issues in intelligent tutoring systems, Proceedings of the International Computer Symposium 2004, 234−239, 2004.
Liu, C.-L., Using mutual information for adaptive item comparison and student assessment, Journal of Educational Technology & Society, 8(4), to appear.
MacQueen, J. B., Some methods for classification and analysis of multivariate observations, Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281−297, 1967.
Millán, E., Pérez-de-la-Cruz, J. L., and Suárez, E., Adaptive Bayesian networks for multilevel student modelling, Proceedings of the Fifth International Conference on Intelligent Tutoring Systems, 534–543, 2000.
Millán, E., and Pérez-de-la-Cruz, J. L., A Bayesian diagnostic algorithm for student modeling and its evaluation, User Modeling and User-Adapted Interaction, 12(2-3), 281−330, 2002.
Manning, S. H., “Foundations of Statistical Natural Language Processing”, MIT Press, 1999.
Martin, J., and VanLehn, K., Student assessment using Bayesian nets, International Journal of Human-Computer Studies, 42(6), 575−591, 1995.
Mislevy, R. J. Probability-based inference in cognitive diagnosis. In Nichols, P., Chipman, S., and Brennan, R., L., eds., Cognitively Diagnostic Assessment, Hillsdale, NJ: Erlbaum, 1995.
Mislevy, R. J., and Gitomer, G. H., The role of probability-based inference in an intelligent tutoring system, User Modeling and User-Adapted Interaction, 5, 253–282, 1996.
Misley, R. J., Almond, R. G., Yan, D., and Steinberg, L. S., Bayes nets in educational assessment: Where do the numbers come from?, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 437–446, 1999.
Neapolitan, R. E., Learning Bayesian Networks, Prentice Hall, 2004.
Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988.
Reye, J., Student modeling based on belief networks, International Journal of Artificial Intelligence in Education, 14, 63−96, 2004.
Rijsbergen, C. J., Information Retrieval, Butterworths, 1979.
Rost, J. and Langeheine, R. (Eds.), Applications of Latent Trait and Latent Class Models in the Social Sciences, Wasmann, 1997.
Stirling, J., Methodus differentialis, sive tractatus de summation et interpolation serierum infinitarium, 1730. English translation by Holliday, J., The Differential Method: A Treatise of the Summation and Interpolation of Infinite Series, 1749.
Tatsuoka, K. K., Rule space: An approach for dealing with misconceptions based on item response theory, Journal of Educational Measurement, 20, 345–354, 1983.
VanLehn, K., Ohlsson, S., and Nason, R., Applications of simulated students: An exploration, International Journal of Artificial Intelligence in Education, 5(2), 135–175, 1994.
VanLehn, K. and Martin, J., Evaluation of an assessment system based on Bayesian student modeling, International Journal of Artificial Intelligence in Education, 8(2), 179–221, 1997.
VanLehn, K., Conceptual and meta learning during coached problem solving, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 29–47, 1996.
Vomlel, J., Bayesian networks in educational testing, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 83−100, 2004.
Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K., Probability and Statistics for Engineers and Scientists, seventh edition, Prentice Hall, 2002.
Yan, D., Almond, R. G., and Mislevy, R. J., Empirical comparisons of cognitive diagnostic models, Technical Report, Educational Testing Service, http://www.ets.org/research/dload/aera03-yan.pdf, 2003.
Zhou, Y. and Evens, M. W., A practical student model in an intelligent tutoring system, Proceedings of the Eleventh IEEE International Conference on Tools with Artificial Intelligence, 13–18, 1999.描述 碩士
國立政治大學
資訊科學學系
92753028
93資料來源 http://thesis.lib.nccu.edu.tw/record/#G0927530281 資料類型 thesis dc.contributor.advisor 劉昭麟 zh_TW dc.contributor.advisor Liu, Chao-Lin en_US dc.contributor.author (Authors) 王鈺婷 zh_TW dc.contributor.author (Authors) Wang, Yu-Ting en_US dc.creator (作者) 王鈺婷 zh_TW dc.creator (作者) Wang, Yu-Ting en_US dc.date (日期) 2004 en_US dc.date.accessioned 17-Sep-2009 14:08:46 (UTC+8) - dc.date.available 17-Sep-2009 14:08:46 (UTC+8) - dc.date.issued (上傳時間) 17-Sep-2009 14:08:46 (UTC+8) - dc.identifier (Other Identifiers) G0927530281 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32727 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學學系 zh_TW dc.description (描述) 92753028 zh_TW dc.description (描述) 93 zh_TW dc.description.abstract (摘要) 本研究以貝氏網路作為表示教學領域中各個學習觀念的關係的語言。教學領域中的學習觀念包含了基本觀念與綜合觀念，綜合觀念是由兩個以上的基本觀念所衍生出來的觀念，而綜合觀念的學習歷程即為學生在學習的過程中如何整合這些基本觀念的過程。了解綜合觀念的學習歷程可以幫助教師及出題者了解學生的學習路徑，並修改其教學或出題的方針，以期能提供適性化的教學及測驗。為了從考生答題資料中尋找出這個隱藏的綜合觀念學習歷程，我們提出一套以mutual information以及一套以chi-square test所發展出來的研究方法，希望能夠藉由一個模擬環境中模擬考生的答題資料來猜測考生學習綜合觀念的學習歷程。初步的實驗結果顯示出，在一些特殊的條件假設下，我們的方法有不錯的機會找到暗藏在模擬系統中的學習歷程。因此我們進而嘗試提出一個策略來尋找較大規模結構中的學習歷程，利用搜尋的概念嘗試是否能較有效率的尋找出學生對於綜合觀念學習歷程。雖然在實驗中並沒有十分理想的結果，但是在實驗的過程中，我們除了發現學生答題資料的模糊程度為系統的正確率的主要挑戰之外，另外也發現了學生類別與觀念能力之間的關係也是影響實驗結果的主要因素。透過我們的方法，雖然不能完美的找出學生對於任何綜合觀念的綜合歷程，但是我們的實驗過程與結果也對隱藏的真實歷程結構提供了不少線索。最後，我們探討如何藉由觀察學生接受測驗的結果來分類不同學習程度與狀況的學生之相關問題與技術。我們利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，探討是否能透過學生的答題資料有效的分辨學生能力的類別。實驗結果顯示出，在每個觀念擁有多道測驗試題的情況下，利用最近鄰居分類法與k-means分群法以及基於這些方法所變化出的方法，藉由考生答題資料來進行學生能力類別的分類可以得到不錯的正確率。我們希望這些探討和結果能對適性化教學作出一些貢獻。 zh_TW dc.description.abstract (摘要) In this thesis, I employ Bayesian networks to represent relations between concepts in pedagogical domains. We consider basic concepts, and composite concepts that are integrated from the basic ones. The learning processes of composite concepts are the ways how students integrate the basic concepts to form the composite concepts. Information about the learning processes can help teachers know the learning paths of students and revise their teaching methods so that teachers can provide adaptive course contents and assessments. In order to find out the latent learning processes based on students’ item response patterns, I propose two methods: a mutual information-based approach and a chi-square test-stimulated heuristics, and examine the ideas in a simulated environment.Results of some preliminary experiments showed that the proposed methods offered satisfactory performance under some particular conditions. Hence, I went a step further to propose a search method that tried to find out the learning process of larger structures in a more efficient way. Although the experimental results for the search method were not very satisfactory, we would find that both the uncertainty included by the students’ item response patterns and the relations between student groups and concepts substantially influenced the performance achieved by the proposed methods. Although the proposed methods did not find out the learning processes perfectly, the experimental processes and results indeed had the potential to provide information about the latent learning processes.Finally, I attempted to classify students’ competence according to their item response patterns. I used the nearest neighbor algorithm, the k-means algorithm, and some variations of these two algorithms to classify students’ competence patterns. Experimental results showed that the more the test items used in the assessment, the higher the accuracy of classification we could obtain. I hope that these experimental results can make contributions towards adaptive learning. en_US dc.description.tableofcontents 第一章 概論...................................................11.1 研究問題與背景............................................11.2 研究目的..................................................51.3 主要成果..................................................61.4 論文結構..................................................7第二章 文獻回顧...............................................82.1 智慧型教學系統與學生模型之相關文獻回顧....................82.2 關於不確定性的相關文獻回顧...............................102.3 應用貝氏網路於學生模型中的相關文獻回顧...................11第三章 問題定義與模擬系統之設計..............................163.1 問題定義.................................................163.2 模擬系統之設計...........................................193.2.1 模擬學生族群...........................................203.2.2 產生學生答題資料.......................................24第四章 研究方法與小規模結構之實驗結果........................294.1 研究方法.................................................294.1.1 基於mutual information所發展出來的計分方法.............294.1.2 基於chi-square test所發展出來的計分方法................314.2 系統設計.................................................344.3 實驗設計、結果與討論.....................................364.3.1 第一組實驗.............................................374.3.1.1 實驗設計.............................................374.3.1.2 實驗結果與討論.......................................384.3.2 第二組實驗.............................................404.3.2.1 實驗設計.............................................404.3.2.2 實驗結果與討論.......................................414.4 綜合討論.................................................46第五章 較大規模結構之實驗方法與結果..........................515.1 研究方法與系統設計.......................................515.2 實驗設計.................................................585.2.1 第一組至第三組實驗之實驗設計...........................585.2.2 第四組實驗之實驗設計...................................625.2.3 第五組實驗之實驗設計...................................635.3 實驗結果與討論...........................................655.3.1 第一組實驗之實驗結果與討論.............................655.3.1.1 以MI-B作為歷程評比指標之實驗結果.....................665.3.1.2 以KS作為歷程評比指標之實驗結果.......................695.3.2 第二組實驗之實驗結果與討論.............................715.3.2.1 以MI-B作為歷程評比指標之實驗結果.....................715.3.2.2 以KS作為歷程評比指標之實驗結果.......................755.3.3 第三組實驗之實驗結果與討論.............................785.3.3.1 以MI-B作為歷程評比指標之實驗結果.....................795.3.3.2 以KS作為歷程評比指標之實驗結果.......................825.3.4 第四組實驗之實驗結果與討論.............................845.3.4.1 以MI-B作為歷程評比指標之實驗結果.....................855.3.4.2 以KS作為歷程評比指標之實驗結果.......................885.3.5 第五組實驗之實驗結果與討論.............................915.3.6 第六組實驗之實驗結果與討論.............................935.4 綜合分析與討論...........................................955.4.1 隱藏的綜合觀念學習歷程結構對於系統尋找綜合觀念學習歷程的影響..........................................................955.4.2 考生答題資料模糊程度對於系統尋找綜合觀念學習歷程的影響.955.4.3 系統所使用之評比指標對於系統尋找綜合觀念學習歷程的影響.965.4.4 cgmatrix對於系統尋找綜合觀念學習歷程的影響.............96第六章 利用考生答題資料分辨學生能力類別之探討................986.1 最近鄰居分類法於分辨學生能力類別的應用...................996.1.1 實驗設計..............................................1006.1.2 實驗結果與討論........................................1036.2 K-means分群法於分辨學生能力類別的應用...................1066.2.1 實驗設計..............................................1086.2.2 實驗結果與討論........................................1086.2.3 增加相同類別的群聚中心來進行k-means分群的嘗試..........1106.2.3.1 實驗設計.............................................1106.2.3.2 實驗結果與討論.......................................111第七章 結論.................................................121參考文獻.....................................................123 zh_TW dc.format.extent 52998 bytes - dc.format.extent 78597 bytes - dc.format.extent 67902 bytes - dc.format.extent 68029 bytes - dc.format.extent 69451 bytes - dc.format.extent 58574 bytes - dc.format.extent 133268 bytes - dc.format.extent 129328 bytes - dc.format.extent 258337 bytes - dc.format.extent 323560 bytes - dc.format.extent 887431 bytes - dc.format.extent 377027 bytes - dc.format.extent 83942 bytes - dc.format.extent 127282 bytes - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.format.mimetype application/pdf - dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0927530281 en_US dc.subject (關鍵詞) 智慧型教學系統 zh_TW dc.subject (關鍵詞) 貝氏網路 zh_TW dc.subject (關鍵詞) 潛在變項分析 zh_TW dc.subject (關鍵詞) 教育評量 zh_TW dc.subject (關鍵詞) 學生模型 zh_TW dc.subject (關鍵詞) Intelligent Tutoring System en_US dc.subject (關鍵詞) Bayesian Networks en_US dc.subject (關鍵詞) Latent Variable Analysis en_US dc.subject (關鍵詞) Educational Assessment en_US dc.subject (關鍵詞) Student Modeling en_US dc.subject (關鍵詞) Automated Cognitive Diagnosis en_US dc.title (題名) 利用貝氏網路建構綜合觀念學習模型之初步研究 zh_TW dc.title (題名) An Exploration of Applying Bayesian Networks for Mapping the Learning Processes of Composite Concepts en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) Abramowitz, M. and Stegun, I. A. (Eds.), Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, ninth printing, New York: Dover, 824−825, 1972. zh_TW dc.relation.reference (參考文獻) Aurenhammer, F., Voronoi diagrams - a survey of a fundamental geometric data structure, ACM Computing Surveys, 23, 345−405, 1991. zh_TW dc.relation.reference (參考文獻) Birenbaum, M., Kelly, A. E., Tatsuoka, K. K., and Gutvirtz, Y., Attribute mastery patterns from rule space as the basis for student models in Algebra, International Journal of Human-Computer Studies, 40(3), 497–508, 1994. zh_TW dc.relation.reference (參考文獻) Brian, T. L., K-Means Clustering, http://fconyx.ncifcrf.gov/~lukeb/kmeans.html, 2005. zh_TW dc.relation.reference (參考文獻) Brusilovsky, P., Schwarz, E., and Weber, G., ELM-ART: An intelligent tutoring system on world wide web, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 261−269, 1996. zh_TW dc.relation.reference (參考文獻) Bunt, A. and Conati, C., Assessing effective exploration in open learning environments using Bayesian networks, Proceedings of the Sixth International Conference on Intelligent Tutoring Systems, 698−707, 2002. zh_TW dc.relation.reference (參考文獻) Bunt, A. and Conati, C., Probabilistic student modeling to improve exploratory behavior, User Modeling and User-Adapted Interaction, 13(3), 269−309, 2003. zh_TW dc.relation.reference (參考文獻) Burns, H. L. and Capps, C. G., Foundations of Intelligent Tutoring Systems: An Introduction, Lawrence Erlbaum Associates, Hillsdale, NJ, 1988. zh_TW dc.relation.reference (參考文獻) Chen, C.-M., Lee, H.-M., and Chen, Y.-H., Personalized e-leaning system using item response theory, Computer & Education, 44(3), 237−255, 2005. zh_TW dc.relation.reference (參考文獻) Collins, J. A., Greer, J. E., and Huang, S. X., Adaptive assessment using granularity hierarchies and Bayesian nets, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 569–577, 1996. zh_TW dc.relation.reference (參考文獻) Conati, C., Gertner, A. S., VanLehn, K., and Druzdzel, M. J., On-line student modeling for coached problem solving using Bayesian networks, Proceedings of the Sixth International Conference on User Modeling, 231−242, 1997. zh_TW dc.relation.reference (參考文獻) Conati, C., Gertner, A. S., and VanLehn, K., Using Bayesian networks to manage uncertainty in student modeling, User Modeling and User-Adapted Interaction, 12, 371–417, 2002. zh_TW dc.relation.reference (參考文獻) Cover, T. and Hart, P., Nearest neighbor pattern classification, Institute of Electrical and Electronics Engineers, Transactions on Information Theory, 13, 21−27, 1967. zh_TW dc.relation.reference (參考文獻) Cover, T. M. and Thomas, J. A., Elements of Information Theory, John-Wiley and Sons, 1991. zh_TW dc.relation.reference (參考文獻) Cowell, R. G., Dawid, A. P., Lauritzen, S. L., and Spiegelhalter, D. J., Probabilistic Networks and Expert Systems, Springer-Verlag, New York, 1999. zh_TW dc.relation.reference (參考文獻) Garthwaite, P. H., Jolliffe, I. T., and Jones, B., Statistical Inference, Prentice Hall, 1995. zh_TW dc.relation.reference (參考文獻) Hambleton, R. K., Swaminathan, H., and Rogers, H. J., Fundamentals of Item Response Theory, Sage Publications, 1991. zh_TW dc.relation.reference (參考文獻) Hatzilygeroudis, I., and Prentzas, J., Knowledge representation requirements for intelligent tutoring systems, Proceedings of the Seventh International Conference on Intelligent Tutoring Systems, 87−97, 2004. zh_TW dc.relation.reference (參考文獻) Heckerman, D. and Breese, J. S., Causal independence for probability assessment and inference using bayesian networks, Institute of Electrical and Electronics Engineers Transactions on Systems, Man, and Cybernetics, 26(6), 826−831,1994. zh_TW dc.relation.reference (參考文獻) Heckerman, D., Learning Bayesian networks, Technical Report MSR-TR-95-02, Microsoft Research, 1995a. zh_TW dc.relation.reference (參考文獻) Heckerman, D., A tutorial on learning with Bayesian networks, Technical Report MSR-TR-95-06, Microsoft Research, 1995b. zh_TW dc.relation.reference (參考文獻) Heckerman, D., Mamdani, A., and Wellman, M. P., Real world applications of Bayesian networks, Communications of the ACM, 38,1995. zh_TW dc.relation.reference (參考文獻) Hsu, C.-N., Chung, H.-H, and Huang, H.-S., Mining skewed and sparse transaction data for personalize shopping recommendation, Machine Learning, 57(1-2), 35–59, 2004. zh_TW dc.relation.reference (參考文獻) Hugin Expert A/S., HUGIN API Reference Manual Version 6.2, http://developer.hugin.com/documentation/API_Manuals, 2004 zh_TW dc.relation.reference (參考文獻) Jensen, F. V., Bayesian Networks and Decision Graphs, Springer, 2001. zh_TW dc.relation.reference (參考文獻) Liu, C.-L., Wang, Y.-T., and Liu, Y.-C., A Bayesian network-based simulation environment for investigating assessment issues in intelligent tutoring systems, Proceedings of the International Computer Symposium 2004, 234−239, 2004. zh_TW dc.relation.reference (參考文獻) Liu, C.-L., Using mutual information for adaptive item comparison and student assessment, Journal of Educational Technology & Society, 8(4), to appear. zh_TW dc.relation.reference (參考文獻) MacQueen, J. B., Some methods for classification and analysis of multivariate observations, Proceedings of Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281−297, 1967. zh_TW dc.relation.reference (參考文獻) Millán, E., Pérez-de-la-Cruz, J. L., and Suárez, E., Adaptive Bayesian networks for multilevel student modelling, Proceedings of the Fifth International Conference on Intelligent Tutoring Systems, 534–543, 2000. zh_TW dc.relation.reference (參考文獻) Millán, E., and Pérez-de-la-Cruz, J. L., A Bayesian diagnostic algorithm for student modeling and its evaluation, User Modeling and User-Adapted Interaction, 12(2-3), 281−330, 2002. zh_TW dc.relation.reference (參考文獻) Manning, S. H., “Foundations of Statistical Natural Language Processing”, MIT Press, 1999. zh_TW dc.relation.reference (參考文獻) Martin, J., and VanLehn, K., Student assessment using Bayesian nets, International Journal of Human-Computer Studies, 42(6), 575−591, 1995. zh_TW dc.relation.reference (參考文獻) Mislevy, R. J. Probability-based inference in cognitive diagnosis. In Nichols, P., Chipman, S., and Brennan, R., L., eds., Cognitively Diagnostic Assessment, Hillsdale, NJ: Erlbaum, 1995. zh_TW dc.relation.reference (參考文獻) Mislevy, R. J., and Gitomer, G. H., The role of probability-based inference in an intelligent tutoring system, User Modeling and User-Adapted Interaction, 5, 253–282, 1996. zh_TW dc.relation.reference (參考文獻) Misley, R. J., Almond, R. G., Yan, D., and Steinberg, L. S., Bayes nets in educational assessment: Where do the numbers come from?, Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 437–446, 1999. zh_TW dc.relation.reference (參考文獻) Neapolitan, R. E., Learning Bayesian Networks, Prentice Hall, 2004. zh_TW dc.relation.reference (參考文獻) Pearl, J., Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, 1988. zh_TW dc.relation.reference (參考文獻) Reye, J., Student modeling based on belief networks, International Journal of Artificial Intelligence in Education, 14, 63−96, 2004. zh_TW dc.relation.reference (參考文獻) Rijsbergen, C. J., Information Retrieval, Butterworths, 1979. zh_TW dc.relation.reference (參考文獻) Rost, J. and Langeheine, R. (Eds.), Applications of Latent Trait and Latent Class Models in the Social Sciences, Wasmann, 1997. zh_TW dc.relation.reference (參考文獻) Stirling, J., Methodus differentialis, sive tractatus de summation et interpolation serierum infinitarium, 1730. English translation by Holliday, J., The Differential Method: A Treatise of the Summation and Interpolation of Infinite Series, 1749. zh_TW dc.relation.reference (參考文獻) Tatsuoka, K. K., Rule space: An approach for dealing with misconceptions based on item response theory, Journal of Educational Measurement, 20, 345–354, 1983. zh_TW dc.relation.reference (參考文獻) VanLehn, K., Ohlsson, S., and Nason, R., Applications of simulated students: An exploration, International Journal of Artificial Intelligence in Education, 5(2), 135–175, 1994. zh_TW dc.relation.reference (參考文獻) VanLehn, K. and Martin, J., Evaluation of an assessment system based on Bayesian student modeling, International Journal of Artificial Intelligence in Education, 8(2), 179–221, 1997. zh_TW dc.relation.reference (參考文獻) VanLehn, K., Conceptual and meta learning during coached problem solving, Proceedings of the Third International Conference on Intelligent Tutoring Systems, 29–47, 1996. zh_TW dc.relation.reference (參考文獻) Vomlel, J., Bayesian networks in educational testing, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 12, 83−100, 2004. zh_TW dc.relation.reference (參考文獻) Walpole, R. E., Myers, R. H., Myers, S. L., and Ye, K., Probability and Statistics for Engineers and Scientists, seventh edition, Prentice Hall, 2002. zh_TW dc.relation.reference (參考文獻) Yan, D., Almond, R. G., and Mislevy, R. J., Empirical comparisons of cognitive diagnostic models, Technical Report, Educational Testing Service, http://www.ets.org/research/dload/aera03-yan.pdf, 2003. zh_TW dc.relation.reference (參考文獻) Zhou, Y. and Evens, M. W., A practical student model in an intelligent tutoring system, Proceedings of the Eleventh IEEE International Conference on Tools with Artificial Intelligence, 13–18, 1999. zh_TW
