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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

    • Kazuma Fuchimoto, Maomi Ueno: Automated Parallel Test Forms Assembly using Integer Programming with Logistic Item Exposure Penalties to Balance the Number of Test Forms while Minimizing Item Exposure Bias, IEEE Access, July 2026 (2026) https://ieeexplore.ieee.org/document/11614802
    • Yoshimitsu Miyazawa, and Maomi Ueno: Process-Integrated IRT: Enhancing Ability Estimation in Computer-based Programming Assessments through Response Process Data. The 27th International Conference on Artificial Intelligence in Education (AIED), 2026, pp. 355-369, Main track, full paper, (2026) CORE2023 ranking A
    • Maomi Ueno, Enbo Zhang, Kazuma Fuchimoto, Satoshi Chiba, Jingde Chen, Chikako Ishizuka: Multi-task deep neural network for predicting both nuclear fission yields and their experimental errors in peak-shaped data, Journal of Nuclear Science and Technology, pp.1-16, (2026) DOI:10.1080/00223131.2026.2652374(PDF)
    • Shouta Sugahara, Koya Kato, James Cussens, Maomi Ueno: Learning Bayesian Network Classifiers to Minimize Class Variable Parameters, Journal of Machine Learning Research, 27(21):1−41, (2026) (PDF)
    • Maomi Ueno (Keynote Speaker): AI-powered Educational Revolution, Keynote Speech, The 20th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2025)

    • Kahori Ogashiwa, Tsuyoshi Okuno, Masanori Takagi , Akihiro Kashihara, Maomi Ueno, Masakazu Muramatsu, and Shunichi Tano: Case Study of UEC’s Novel Learning Environment for Cultivating Engineering Talents, IIAI Open Conference Publication SeriesIIAI Letters on Informatics and Interdisciplinary ResearchVol.006, LIIR395, pp1-6, (2025), DOI: https://doi.org/10.52731/liir.v006.395


    • Maomi Ueno, Yoshimitsu Miyazawa, and Emiko Tsutsumi: Probability-Based Scaffolding System Using Sliding Hidden Markov IRT for Longitudinal Learning. The 26th International Conference on Artificial Intelligence in Education (AIED), 2025, pp. 448-462, Main track, full paper, (2025) (PDF) CORE2023 ranking A

    • Maomi Ueno, Kazuma Fuchimoto, Wakaba Kishida, and Yoshimitsu Miyazawa: Computerized Adaptive Testing to Balance Exposure Bias and Measurement Accuracy using Zero-suppressed Binary Decision Diagrams. IEEE Access, Volume 13, pp. 33883-33903, (2025) DOI: 10.1109/ACCESS.2025.3543554

    • Masaki Uto, Jun Tsuruta, Kouji Araki, Maomi Ueno: Item response theory model highlighting rating scale of a rubric and rater-rubric interaction in objective structured clinical examination. PLOS ONE, 19 (9), pp.e0309887-e0309887 (2024) (PDF)

    • Emiko Tsutsumi, Tetsurou Nishio and Maomi Ueno: Deep-IRT with a Temporal Convolutional Network for Reflecting Students’ Long-Term History of Ability Data. The 25th International Conference on Artificial Intelligence in Education (AIED), 2024, (PDF) CORE2023 ranking A

    • Shouta Sugahara, Koya Kato and Maomi Ueno: Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters. Proceedings of the AAAI Conference on Artificial Intelligence, 38(18), 20540-20549. (2024) https://doi.org/10.1609/aaai.v38i18.30039. (PDF)
      (Supplementary-material) CORE2023 ranking A*

    • 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, 17: 951-965 (2024) (PDF)

    • Kazuma Fuchimoto, Shin-ichi Minato, Maomi Ueno: Automated Parallel Test Forms Assembly using Zero-suppressed Binary Decision Diagrams, IEEE Access, Oct 2023.

    • 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. The 24th International Conference on Artificial Intelligence in Education (AIED), 2023 (Late Breaking Results)

    • 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.

    • 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. CORE2022 ranking B

    • Maomi Ueno, Yoshimitsu Miyazawa: Two-Stage Uniform Adaptive Testing to Balance Measurement Accuracy and Item Exposure. – 23rd International Conference on Artificial Intelligence in Education (AIED), (1) 2022, 626-632, 2022. CORE2022 ranking A

    • 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).

    • 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, The 22th International Conference on Artificial Intelligence in Education (AIED), 2021(2), 54-59, 2021 (PDF) CORE2021 ranking A

    • 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) CORE2021 ranking B

    • 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, The 20th International Conference on Artificial Intelligence in Education, Lecture Notes in Artificial Intelligence, LNAI11625, AIED 2019, 482-493, CORE2018 ranking A

    • 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).

    • 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, The 19th International Conference on Artificial intelligence in Education (AIED), 2018(2), 363-368,(PDF) CORE2018 ranking A

    • 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, The 18th International Conference on Artificial Intelligence in Education (AIED), 2017, 393-405,(PDF) CORE2017 ranking A

    • Takatoshi Ishii and Maomi Ueno: Algorithm for Uniform Test Assembly Using a Maximum Clique Problem and Integer Programming, The 18th International Conference on Artificial Intelligence in Education (AIED), 2017, 102-112,(PDF) CORE2017 ranking A

    • 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) CORE2015 ranking B

    • 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) CORE2015 raking B

    • Maomi Ueno and Yoshimitsu Miyasawa: Probability Based Scaffolding System with Fading, Artificial Intelligence in Education – 17th International Conference (AIED), 2015, 237-246,(PDF) CORE2015 ranking A

    • 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) CORE2015 ranking A

    • 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) CORE2015 ranking A

    • 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) CORE2015 ranking A

    • 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) CORE2014 ranking B

    • Yoshimitsu Miyasawa, Maomi Ueno: Mobile Testing for Authentic Assessment in the Field. Artificial Intelligence in Education – 16th International Conference (AIED), 2013,619-623,(PDF)　CORE2013 ranking A

    • 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)　CORE2013 ranking A

    • Maomi Ueno: Adaptive Testing Based on Bayesian Decision Theory Artificial Intelligence in Education – 16th International Conference (AIED), 2013, 712-716,(PDF) CORE2013 ranking A

    • 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) CORE2011 ranking A*

    • 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) CORE2010 ranking A*

    • Maomi Ueno: Advanced technologies for e-testing, Proc. The 18th International Conference on Computers in Education, (ICCE) (2010),(Invited speech),(PDF) CORE2010 ranking B

    • 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) CORE2009 ranking A

    • 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) CORE2008 ranking B

    • 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)　CORE2008 ranking B

    • 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) CORE2008 ranking B

    • 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)　CORE2008 ranking B

    • 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)　CORE2007 ranking B

    • 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)　CORE2006 ranking B

    • 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) CORE2005 ranking B

    • Maomi Ueno, Keizo Nagaoka, On-Line Analysis of e-Learning Time based on Gamma Distributions, Proc. ED-Media(full paper),3629-3637（2005） CORE2005 ranking B

    • 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) CORE2004 ranking B

    • Maomi Ueno: Data mining and text mining technologies for collaborative learning in an ILMS “Ssamurai”, IEEE International Conference on Advanced Learning Technologies, 2004. Proceedings., Joensuu, Finland, 2004, pp. 1052-1053, doi: 10.1109/ICALT.2004.1357749.

    • Maomi Ueno: On-Line Contents Analysis System for e-Learning, Proc.IEEE conference (Computer Science) ICALT2004, 762-764 (2004),(PDF) CORE2004 ranking B

    • 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) CORE2004 ranking B

    • 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） CORE2004 ranking B

    • 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）CORE2003 ranking B

    • 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) CORE2002 ranking B

    • Maomi Ueno, Fumio Yoshida: Web based Computerized Testing System, Proc. International Conference on Advanced Learning Technologies 2002, IEEE Computer Science, 534-538（2002） CORE2002 ranking B

    • 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) CORE2001 ranking B

    • Maomi Ueno & Keizo Nagaoka: Web based Computerized Testing System for Distance Education(as a full paper), Proc. ICCE 2001, 547-554, (2001),(PDF) CORE2001 ranking B

    • 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） CORE2000 ranking B

    • 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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