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電気通信大学大学院情報理工学研究科
情報・ネットワーク工学専攻
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International journals, conferences

    • Shouta Sugahara, Koya Kato and Maomi Ueno: Learning Bayesian Network Classifiers to Minimize Class Variable Parameters. In the 38th AAAI Conference on Artificial Intelligence (AAAI 2024).(PDF)
    • Emiko Tsutsumi, Yiming Guo, Ryo Kinoshita, Maomi Ueno: Deep knowledge tracing incorporating a
      hypernetwork with independent student and item networks, IEEE Transactions on Learning Technologies, doi: 10.1109/TLT.2023.3346671 (2023)(PDF)
    • Kazuma Fuchimoto, Shin-ichi Minato, Maomi Ueno: Automated Parallel Test Forms Assembly using Zero-suppressed Binary Decision Diagrams, IEEE Access, Oct 2023 (PDF)
    • Masaki Uto, Itsuki Aomi, Emiko Tsutsumi, Maomi Ueno:Integration of Prediction Scores From Various Automated Essay Scoring Models Using Item Response Theory, IEEE Transactions on Learning Technologies, Volume 16, Issue 6, PP 983-1000, December 2023(2023) (PDF)
    • Wakaba Kishida, Kazuma Fuchimoto, Yoshimitsu Miyazawa and Maomi Ueno: Item difficulty constrained uniform adaptive testing. Artificial Intelligence in Eduation – 24th International Conference, AIED 2023 (PDF)
    • Shouta Sugahara, Itsuki Aomi, and Maomi Ueno: Bayesian Network Model Averaging Classifiers by Subbagging. Entropy 2022, 24(5), 743; https://doi.org/10.3390/e24050743. (PDF)
    • Kazuma Fuchimoto, Takatoshi Ishii, and Maomi Ueno: Hybrid Maximum Clique Algorithm Using Parallel Integer Programming for Uniform Test Assembly,IEEE Transactions on Learning Technologies, vol. 15, no. 2, pp. 252-264, 1 April 2022,doi: 10.1109/TLT.2022.3163360.(PDF)
    • Shouta Sugahara, Wakaba Kishida, Koya Kato, Maomi Ueno: Recursive autonomy identification-based learning of augmented naive Bayes classifiers, The 11th International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research 2022, volume 186, pages 265–276.
    • Emiko Tsutsumi, Yiming Guo, Maomi Ueno: Deep knowledge tracing in corporating a hypernetwork with independent student and item networks, Proceedings of the 15th International Conference on Educational Data Mining (EDM), 2022
    • Maomi Ueno, Yoshimitsu Miyazawa: Two-Stage Uniform Adaptive Testing to Balance Measurement Accuracy and Item Exposure. Artificial Intelligence in Education – 23rd International Conference, AIED (1) 2022, 626-632, 2022(PDF)
    • Shouta Sugahara, Maomi Ueno: Exact Learning Augmented Naive Bayes Classifier. Entropy 2021, 23, 1703.https://doi.org/10.3390/ e23121703,2021
      (PDF)
    • Maomi Ueno, Kazuma Fuchimoto, and Emiko Tsutsumi:E-testing from artificial intelligence approach. Behaviormetrika, Vol. 48, No. 2, pp. 409–424, 2021. (Invited Paper)(PDF)
    • Emiko Tsutsumi, Ryo Kinoshita, Maomi Ueno :Deep Item Response Theory as a Novel Test Theory Based on Deep Learning, electronics, Vol.10, Issue.9, no.1020 (2021)(PDF)
    • Maomi Ueno: AI based e-Testing as a common yardstick for measuring human abilities, 18th International Joint Conference on Computer Science and Software Engineering (JCSSE), IEEE, 2021(PDF)
    • Itsuki Aomi, Emiko Tsutsumi, Masaki Uto, Maomi Ueno: Integration of Automated Essay Scoring Models using Item Response Theory, Artificial Intelligence in Education – 22th International Conference, AIED 2021(2), 54-59, 2021 (PDF)
    • Emiko Tsutsumi, Ryo Kinoshita, Maomi Ueno: Deep-IRT with independent student and item networks, Proceedings of the 14th International Conference on Educational Data Mining (EDM), 2021 (PDF)
    • Masaki Uto, Maomi Ueno:A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo, Behaviormetrika, Springer, Vol.47, Issue.2, pp.469-496 (2020),(PDF)
    • Masaki Uto, Duc-Thien Nguyen, Maomi Ueno:Group optimization to maximize peer assessment accuracy using item response
      theory and integer programming, IEEE Transactions on Learning Technologies, Vol.13, Issue 1, pp.91-106 (2020), (PDF)
    • Shouta Sugahara, Itsuki Aomi, Maomi Ueno: Bayesian Network Model Averaging Classifiers by Subbagging, The 10th International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research 2020, volume 138, pages 461–472,(PDF)
    • Masaki Uto, Yikuan Xie, Maomi Ueno:Neural Automated Essay Scoring Incorporating Handcrafted Features. COLING 2020:The 28th International Conference on Computational Linguistics, pp, 6077-6088(PDF)
    • Masaki Uto and Maomi Ueno: Empirical comparison of item response theory models with rater’s parameters, Heliyon, Vol. 4, Issue 5, P. e00622, Elsevier (2018),(PDF)
    • Yoshimitsu Miyazawa, Maomi Ueno:Computerized Adaptive Testing Method Using Integer Programming to Minimize Item Exposure. JSAI 2019: Advances in Artificial Intelligence, Springer, pp 105-113, 2019
    • Maomi Ueno and Yoshimitsu Miyazawa: Uniform adaptive testing using maximum clique algorithm, Artificial Intelligence in Education – 20th International Conference, Lecture Notes in Artificial Intelligence, LNAI11625, AIED 2019, 482-493,(PDF)
    • Maomi Ueno, Yoshimitsu Miyazawa: IRT-Based Adaptive Hints to Scaffold Learning in Programming, IEEE Transactions on Learning Technologies, IEEE computer Society, Vol.11, Issue 4, pp.415-428 (2018),(PDF)
    • Shouta Sugahara, Masaki Uto, Maomi Ueno: Exact Learning Augmented Naive Bayes Classifier, The 9th International Conference on Probabilistic Graphical Models (PGM), Proceedings of Machine Learning Research 2018, volume 72, pages 439–450,(PDF)
    • Masaki Uto and Maomi Ueno: Item Response Theory Without Restriction of Equal Interval Scale for Rater’s Score, Artificial Intelligence in Education – 19th International Conference, AIED 2018(2), 363-368,(PDF)
    • Minoru Nakayama, Katsuaki Suzuki, Chiharu Kogo, Maomi Ueno: Curriculum development for Educational Technology based on comparisons of course syllabi resources using lexical analysis, EAI Endorsed Transactions on e-Learning 4(16), pp.1-8 (2017),(PDF)
    • Chao Li, Maomi Ueno: An extended depth-first search algorithm for optimal triangulation of Bayesian networks, International Journal of Approximate Reasoning, Volume 80 Issue C, pp.294-312 (2017),(PDF)
    • Kazuki Natori, Masaki Uto and Maomi Ueno: Consistent Learning Bayesian Networks with Thousands of Variables, The 3rd International Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2017, Proceedings of Machine Learning Research (PMLR) 73:57-68
    • Taiyo Utsuhara, Masaki Uto, Asana Ishihara, Atsushi Yoshikawa, Maomi Ueno: Classification of Japanese Graduate Schools: In terms of educational practices and the grown globalization competencies by the policies, International Federation of Classification Societies, CN02,(2017)
    • Masaki Uto, Nguyen Duc Thien and Maomi Ueno: Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory, Artificial Intelligence in Education – 18th International Conference, AIED 2017, 393-405,(PDF)
    • Takatoshi Ishii and Maomi Ueno: Algorithm for Uniform Test Assembly Using a Maximum Clique Problem and Integer Programming, Artificial Intelligence in Education – 18th International Conference, AIED 2017, 102-112,(PDF)
    • Taiyo Utsuhara, Masaki Uto, Asana Ishihara, Koichi Ota, Ayako Hirano, Atsushi Yoshikawa, Maomi Ueno: Features of Globalization in Japanese Graduate Schools, International Conference on Education, 392_1-392_10,(2017)
    • Masaki Uto and Maomi Ueno, “Item Response Theory for Peer Assessment”, Item Response Theory for Peer Assessment”, IEEE Transactions on Learning Technologies, vol.9, no. 2, IEEE computer Society, pp.157-170(2016) (PDF)
    • Thien Nguyen, Masaki Uto, Yu Abe and Maomi Ueno: Reliable Peer Assessment for Team-project-based Learning using Item Response Theory, International Conference on Computers in Education, ICCE 2015, 144-153,(PDF)
    • Chao Li and Maomi Ueno: A Fast Clique Maintenance algorithm for Optimal Triangulation of Bayesian Networks, The 2nd Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015,(PDF)
    • Kazuki Natori, Masaki Uto, Yu Nishiyama, Shuichi Kawano and Maomi Ueno: Constraint-based learning Bayesian networks using Bayes factor, The 2nd Workshop on Advanced Methodologies for Bayesian Networks, AMBN 2015, 9505, 15-31,(PDF)
    • Masaki Uto and Maomi Ueno: Academic Writing Support System Using Bayesian Networks, IEEE International Conference on Advanced Learning Technologies, ICALT 2015: 385-387,(PDF)
    • Maomi Ueno and Yoshimitsu Miyasawa: Probability Based Scaffolding System with Fading, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 237-246,(PDF)
    • Takatoshi Ishii and Maomi Ueno: Clique Algorithm to Minimize Item Exposure for Uniform Test Forms Assembly, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 638-641,(PDF)
    • Masaki Uto and Maomi Ueno: Item Response Model with Lower Order Parameters for Peer Assessment, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 800-803,(PDF)
    • Sébastien Louvigné, Yoshihiro Kato, Neil Rubens, and Maomi Ueno: SNS Messages Recommendation for Learning Motivation, Artificial Intelligence in Education – 17th International Conference, AIED 2015, 237-246,(PDF)
    • Takatoshi Ishii, Pokpong Songmuang, Maomi Ueno: Maximum Clique Algorithm and ItsApproximation for UniformTest Form Assembly, IEEE Transactions on Learning Technologies,7(1) IEEE computer Society,1-13,2014,(PDF)
    • Sébastien Louvigné, Yoshihiro Kato, Neil Rubens, Maomi Ueno: Goal-Based Messages Recommendation Utilizing Latent Dirichlet Allocation, IEEE International Conference on Advanced Learning Technologies, 2014: 464-468,(PDF)
    • Yoshimitsu Miyasawa, Maomi Ueno: Mobile Testing for Authentic Assessment in the Field. Artificial Intelligence in Education – 16th International Conference, AIED 2013,619-623,(PDF)
    • Takatoshi Ishii, Pokpong Songmuang, Maomi Ueno: Maximum Clique Algorithm for Uniform Test Forms Assembly, Artificial Intelligence in Education – 16th International Conference, AIED 2013,451-462,(PDF)
    • Maomi Ueno: Adaptive Testing Based on Bayesian Decision Theory Artificial Intelligence in Education – 16th International Conference, AIED 2013, 712-716,(PDF)
    • Maomi Ueno and Masaki Uto: Non-Informative Dirichlet Score for learning Bayesian networks, Proc. The Sixth European Workshop on Probabilistic Graphical Models(PGM), 331-338 (2012),(PDF)
    • Chao Li and Maomi Ueno: A Depth-First Search Algorithm for Optimal Triangulation of Bayesian Network, Proc. The Sixth European Workshop on Probabilistic Graphical Models(PGM), 187-194 (2012),(PDF)
    • Pokpong Songmuang and Maomi Ueno: Bees Algorithm for Construction of Multiple Test Forms in E-Testing, IEEE Transactions on Learning Technologies, IEEE computer Society, Vol. 4, No. 3, 209-221(2011),(PDF)
    • Takamitsu Hashimoto and Maomi Ueno: Latent Conditional Independence Test Using Bayesian Network Item Response Theory, IEICE Transactions on Information and Systems, Vol.E94.D, No.4, 743-753(2011),(PDF)
    • Maomi Ueno: Robust learning Bayesian networks for prior belief, AUAI Press (UAI) Proc. The Twenty-Seventh Conference of Uncertainty in Artificial Intelligence, 698-707(2011),(PDF)
    • Maomi Ueno: Learning networks determined by the ratio of prior and data, AUAI Press (UAI) Proc. The Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, 598-605(2010),(PDF)
    • Maomi Ueno: Advanced technologies for e-testing, Proc. The 18th International Conference on Computers in Education, (ICCE) (2010),(Invited speech),(PDF)
    • Minoru Nakayama and Maomi Ueno: Current educational technology research trends in Japan, Educational Technology Research and Development, Vol.57, No.2, 271-285(2009),(PDF)
    • Maomi Ueno: Intelligent LMS with an agent that learns from log data, Journal of Information and Systems in Education, Vol.7, No.1, 3-14(2009),(PDF)
    • Takashi Isozaki, Nojiri Kato, and Maomi Ueno: Data temperature” in minimum free energies for parameter learning of Bayesian networks, International Journal on Artificial Intelligence Tools, Vol.18, No.5, 653-671(2009)
    • Takashi Isozaki and Maomi Ueno:  Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks, ECML PKDD 2009, Machine Learning and Knowledge Discovery in Databases, European Conference, LNAI 5789, 612-627(2009),(PDF)
    • Maomi Ueno: Learning likelihood-equivalence Bayesian networks using an empirical Bayesian approach, Behaviormetrika, Vol.35, No.2, 115-135(2008),(PDF)
    • Maomi Ueno, Takahiro Yamazaki: Collaborative filtering for massive datasets based on Bayesian networks, Behaviormetrika, Vol.35, No.2, 137-158(2008),(PDF)
    • Takashi Isozaki and Maomi Ueno: Minimum Free Energies with “Data Temperature” for Parameter Learning of Bayesian Networks,  Proc. The 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008),371-378 (2008): Best Paper Award,(PDF)
    • Maomi Ueno and Toshio Okamoto: Item Response Theory for Peer Assessment, Proc. The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008 554-558(2008),(PDF)
    • Masahiro Ando and Maomi Ueno: Effect of pointer presentation on multimedia e-learning materials, Proc.World Conference on Educational Multimedia, Hypermedia &Telecommunications(ED-MEDIA 2008), 5549-5559: Outstanding Paper Award,(PDF)
    • Maomi Ueno, Toshio Okamoto: System for Online Detection of Aberrant Responses in E-Testing, Proc. The 8th IEEE International Conference on Advanced Learning Technologies, ICALT 2008, 824-828 (2008),(PDF)
    • Maomi Ueno: Learning Bayesian networks from an empirical Bayes approach, Proc. Int. Conf. on International Association for Statistical Computing, Invited Session on Bayesian statistics (2008) (invited),
    • Maomi Ueno and Toshio Okamoto: Bayesian Agent in e-Learning, Proc.The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007, 282-284(2007),(PDF)
    • Yasuhiko Morimoto, Maomi Ueno, Isao Kikukawa, Setsuo Yokoyama, Youzou Miyadera, SALMS: SCORM-compliant Adaptive LMS, Proc. the 12th World Conference on E-Learning in Corporate, Government, Healthcare, & Higher Education (E-Learn2007), 7287-7296 (2007) : Outstanding Paper Award
    • Yasuhiko Morimoto, Maomi Ueno,Isao Kikukawa,Setsuo Yokoyama and Youzou Miyadera : Formal Method of Description Supporting Portfolio Assessment, International Journal of Educational Technology & Society, Vol. 9, No. 3, 88-99(2006),(PDF)
    • Maomi Ueno and Toshio Okamoto, Intelligent Bayesian agent as a facilitator in e-Learning. Proc. E-Learn2006, 3084-3092 (2006)
    • Maomi Ueno and Toshio Okamoto, Online MDL-Markov analysis of a discussion process in CSCL, ICALT ’06: Proc. the Sixth IEEE International Conference on Advanced Learning Technologies, 764-768(2006),(PDF)
    • Maomi Ueno: Web based computerized testing system for distance education, Educational Technology Research, Vol.28, No.1・2, 59-69(2005),(PDF)
    • Maomi Ueno: Evaluation of e-Learning contents presentation methods using an eye mark recorder, Proc.the 2nd Joint Workshop of Cognition and Learning through Media-Communication for Advanced e-Learning, 207-212(2005)
    • Maomi Ueno: Intelligent LMS with an agent that learns from log data, Proc. e-Learn2005, 3169-3176(2005): Outstanding Paper AwardMaomi Ueno: New pedagogies and vocational education, Proc. UNISCO-UNEVOC /JSISE International Seminar,  Invited Speach, pp.153-166(2005)(invited)
    • Maomi Ueno: Animated Pedagogical Agent based on Decision Tree for e-Learning, Proc.IEEE conference (Computer Science), ICALT(2005),(PDF)
    • Maomi Ueno, Keizo Nagaoka, On-Line Analysis of e-Learning Time based on Gamma Distributions, Proc. ED-Media(full paper),3629-3637(2005)
    • Maomi Ueno: e-Learning in Technical and Vocational Education and Training, Journal for Vocational and Technical Education and Training, Vol.4, No.2, 53-65(2004)
    • Maomi Ueno: An Unified Derivation of Various IRT Models from Bayesian Approach, Proc.The 82nd Symposium of the Behaviormetric Society of Japan, Recent Developments in Latent Variables Modeling,83-99(2004)(invited)
    • Maomi Ueno: Data mining and text mining technologies for collaborative learning in LMS “SAMURAI”, Proc.IEEE International Conference, Special Panel “Collaborative Technology and New e-Pedagogy, Proc.IEEE conference (Computer Science), ICALT2004 2004, 1052-1053 (2004) (invited),(PDF)
    • Maomi Ueno: On-Line Contents Analysis System for e-Learning, Proc.IEEE conference (Computer Science) ICALT2004, 762-764 (2004),(PDF)
    • Maomi Ueno: Animated agent to maintain learner’s attention in e-learning , Proc. E-Learn2004 (2004) :Outstanding Paper Award,(PDF)
    • Yasuhiko Morimoto, Maomi Ueno, Nobuyuyoshi Yonezawa, Setsuo Yokoyama, Youzou Miyadera: A Meta-Language for Portfolio Assessment, Proc.IEEE conference (Computer Science) ICALT2004,  2004, 46-50 (2004),(PDF)
    • Maomi Ueno, Tetsuya Kimura,  Alfred Neudorfer, Rupert Maclean: e-learning on TVET between Japan and Germany, Proc. ITHET 2004(Full paper), in Istanbul(2004),(PDF)
    • Maomi Ueno: Evaluation of E-Learning Contents Presentation methods using Eye Mark Recorder, Proc. ED-Media(Full paper) in Lugano(2004)
    • Maomi Ueno: Technical and Vocational Education based on ICT, Proc.International Research Conference Education and Training(2004)(invited speech)
    • Maomi Ueno: Online Outlier Detection System for Learning Time Data in E-Learning and It’s evaluation, Proc. Computers and Advanced Technology in Education(CATE2004)(2004),(PDF)
    • Maomi Ueno: Learning Log Database and Data Mining system for e-Learning -On-Line Statistical Outlier Detection of irregular learning processes-, Invited talk, Proc. The 6th Sanken ISIR International Symposium, New Trends in Knowledge Proceedings, 147-150(2003)(invited)
    • Maomi Ueno: LMS with irregular learning processes detection system, Proc. E-learn2003, pp.2486-2493(2003)
    • Maomi Ueno: Online statistical outlier detection of irregular learning processes for e-learning, Proc. ED-Media(Full paper) in Hawaii pp.227-234(2003)
    • Maomi Ueno: Technical and Vocational Education in Japan, Invited Speech in UNESCO TVE seminar, Mongolia(2003)
    • Maomi Ueno: E-learning between Universities and Japanese National Colleges of Technology, Proc. ITHET2003 (Full Paper) in Morocco , 121-129(2003)
    • Keizo Nagaoka, Hiroshi Kato, Toshihisa Nishimori, Maomi Ueno: Distant IT Course and IT Counseling System over a City-based Broadband Area Network connected via Laser Beam Transmitter, Proc. ITHET2003 (Full Paper)in Morocco  91-97(2003)
    • Maomi Ueno: An extension of the IRT to a network model, Behaviormetrika, Vol.29, No.1, 59-79(2002),(PDF)
    • Maomi Ueno & Keizo Nagaoka: Web based response analyzer for distance, education(full paper), Proc. Intertech 2002, Santos-Brazil(2002)
    • Maomi Ueno, Keizo Nagaoka: Learning Log Database and Data Mining system for e-Learning -On-Line Statistical Outlier Detection of irregular learning processes-, Proc. International Conference on Advanced Learning Technologies 2002, IEEE Computer Science, 436-438(2002),(PDF)
    • Maomi Ueno, Fumio Yoshida: Web based Computerized Testing System, Proc. International Conference on Advanced Learning Technologies 2002, IEEE Computer Science, 534-538(2002)
    • Maomi Ueno: Joint discrete probabilities distribution, Invited Lecture, in KU Laven in Berugium(2001)(invited)
    • Maomi Ueno : An unified derivation of various IRT models from Bayesian approach, Proc. International Meeting of the Psychometric Society, 196-197(2001)
    • Maomi Ueno: Student models Construction by using Information Criteria(as a full paper), Proc. IEEE International Conference on Advanced Learning Technologies (published by IEEE Computer Society),331-334, (2001),(PDF)
    • Maomi Ueno & Keizo Nagaoka: Web based Computerized Testing System for Distance Education(as a full paper), Proc. ICCE 2001, 547-554, (2001),(PDF)
    • Yoshiki Mikami, Maomi Ueno, Yoshida Fumio, Ishibashi Takazumi, Suzuki Izumi:  Distance Learning and Web Based Learning in Technical Education : A case at Nagaoka University of Technology Japan, Proc. Saudi Technical Conference and Exhibition(2000)
    • Maomi Ueno: Intelligent Tutoring System based on belief networks, Proc. IEEE International Conference on Advanced Learning Technologies, Computer Science(2000)
    • Maomi Ueno and Peter Bearse: A unified Approach to Information-Theoretic and Bayesian Model Selection Criteria, INTERNATIONAL SOCIETY for BAYESIAN ANALYSIS, Proc. 6th WORLD MEETING (2000)(Hersonissos-Heraklion, Crete) (Invited)
    • Maomi Ueno: derivation of discrete joint probability, Proc. Joint Statistical Meetings, American Statistical Association(1999)
    • Maomi Ueno: An asymptotic analysis of log-likelihood of Bayesian networks, Proc. Information-Based Induction Science(1999)
    • Kwon, Son .Hak., Maomi Ueno, and Michio Sugeno: A consistent and bias corrected extension of Akaike Information Criterion (AIC), The society for Industrial and Applied Mathematics, Vol.2, No.1, 41-60(1998)
    • Maomi Ueno :Expanded Bayesian Model Selection, Proc. The 6th conference of the International Federation of Classification Societies 98(1998)
    • Maomi Ueno :Open Testing System, Proc. Open Learning International Conference 98(1998)
    • Maomi Ueno :Bias-Corrected Bayesian Model Selection, Proc.The 6th Japan China Statistical Symposium(1997)
    • Maomi Ueno, Hitoshi Ohnishi, and Kazuo Shigemasu: Proposal of a test theory with probabilistic network, Electronics and Communications, John Willy and Sons, Inc., Vol.78, No.5, 54-66(1995)
    • Maomi Ueno. and Keizo Nagaoka: A model for multiple-choice problem selection, Electronics and Communications, John Willy & Sons, Inc. Company, Vol.77, Issue 2, 14-23(1994)
    • Kazuo Shigemasu and Maomi Ueno:  A new item response model with parameters reflecting state of knowledge, Behaviormetrika, Vol.20, No.2, 161-169 (1993),(PDF)
    • Maomi Ueno and Nagaoka, K.: The development of a computer-assisted test construction system in consideration of evaluation of learner’s response speed, Proc.’ICOMMET’ 91, 13-15(1991)
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