Dr. of Engineering, Tokyo Institute of Technology

Data Science

Bayes

Machine Learning

Artificial Intelligence

Humanities & social sciences, Educational technology

Informatics, Statistical science

Informatics, Mathematical informatics

Apr. 2016 - Present
The University of Electro-Communications, Graduate School of Informatics and Engineering Department of Computer and Network Engineering, 教授, Japan

Sep. 2013 - Mar. 2016
The University of Electro-Communications, Graduate School of Information Systems Department of Social Intelligence and Informatics, 教授, Japan

Jan. 2007 - Aug. 2013
The University of Electro-Communications, Graduate School of Information Systems Department of Social Intelligence and Informatics, 准教授, Japan

Apr. 2006 - Dec. 2007
The University of Electro-Communications, Graduate School of Information Systems Department of Social Intelligence and Informatics, 助教授, Japan

Apr. 2000 - Mar. 2006
Nagaoka University of Technology, 工学部 経営情報系, 助教授, Japan

Apr. 1996 - Mar. 2000
Chiba University, 文学部 認定情報科学講座, 助手, Japan

Apr. 1994 - Mar. 1996
Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and Engineering, 助手, Japan

Apr. 1992 - Mar. 1994
Tokyo Institute of Technology, 総合理工学研究科, 博士課程システム科学専攻, Japan

Apr. 1990 - Mar. 1992
神戸大学大学院, 教育学研究科, 学校教育専攻, Japan

Apr. 1985 - Mar. 1989
神戸大学, 教育学部, 初等教育課, Japan

Apr. 1981 - Mar. 1984
Sandagakuen high school

Apr. 2021 - Present
海洋AIアドバイザリーボード委員, 東京海洋大学, Others

Apr. 2025 - Mar. 2027
医学生共用試験CBT到達基準検討委員会, 医療系大学間共用試験実施評価機構

Aug. 2024 - Jul. 2026
試験問題検討委員会（分析評価部会）委員, 安全衛生技術試験協会, Society

Apr. 2025 - Mar. 2026
医学生共用試験CBT基準集団検討専門部会委員, 医療系大学間共用試験実施評価機構

Apr. 2024 - Mar. 2026
共用試験信頼性妥当性検討委員会委員, 医療系大学間共用試験実施評価機構

Feb. 2025 - Jul. 2025
Senior Program Committee member, The 18th International Conference on Education Data Mining, Society

Feb. 2025 - Jul. 2025
Senior Program Committee member, 26th International Conference on Education Data Mining (AIED)

Jul. 2023 - Jun. 2025
入試委員会専門委員, 一般社団法人国立大学協会, Society

Apr. 2023 - Mar. 2025
医学系CBT到達基準検討委員会委員, 医療系大学間共用試験実施評価機構

Apr. 2023 - Mar. 2025
試験信頼性妥当性検討委員会委員, 医療系大学間共用試験実施評価機構, Society

Jan. 2024 - Jul. 2024
Publishing chair, Senior Program Committee member, The 17th International Conference on Education Data Mining

Dec. 2023 - Jul. 2024
Wide AIED track chair, Senior Program Committee member, 25th International Conference on Artificial Intelligence in Education (AIED), Society

Apr. 2023 - Mar. 2024
医学系CBT到達基準検討委員会基準集団検討専門部会委員, 医療系大学間共用試験実施評価機構

Apr. 2022 - Jul. 2023
Local Organizing chair, Senior Program Committee member, 24th International Conference on Artificial Intelligence in Education (AIED2023)

Apr. 2022 - Mar. 2023
CBT活用に関するワーキングチーム委員, 大学入試センター

Apr. 2021 - Mar. 2023
委員, 文部科学省 統計エキスパート人材育成プロジェクト推進委員会

Jan. 2022 - Jul. 2022
Senior Program Committee member, 23rd International Conference on Artificial Intelligence in Education (AIED2022), Society

Jun. 2020 - Mar. 2022
CBT活用検討部会委員, 大学入試センター

2022 - 2022
Program Commitee member, The Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI-22), Society

2021 - 2021
Program Committee member, 22th International Conference on Artificial Intelligence in Education (AIED2021), Society

2021 - 2021
Program Commitee member, AAAI 2021 Workshop on AI Education (TIPCE2021), Society

2021 - 2021
Program Committee member, The Thirty-fifth AAAI Conference on Artificial Intelligence (AAAI-21), Society

Jun. 2018 - 2021
理事長, 日本行動計量学会, Society

2017 - 2021
客員研究委員（確率モデリング）, 産業技術総合研究所 人口知能研究センター

Jun. 2019 - May 2020
CBTの活用に関する有識者会議委員, 大学入試センター

2020 - 2020
Program Committee member, 21st International Conference on Artificial Intelligence in Education (AIED2020), Society

2020 - 2020
Program Committee member, The Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI-20), Society

Dec. 2019
専門委員（統計学）, 科学研究費委員会

2019 - 2019
Program Committee member, The Thirty-third AAAI Conference on Artificial Intelligence (AAAI-19), Society

2019 - 2019
Program Committee member, 20th International Conference on Artificial Intelligence in Education (AIED2019), Society

Apr. 2005 - Mar. 2018
理事, 日本行動計量学会, Society

2018 - 2018
Program Committee member, The Thirty-second AAAI Conference on Artificial Intelligence (AAAI-18), Society

2018 - 2018
Program Committee member, 19th International Conference on Artificial Intelligence in Education (AIED2018), Society

2017 - 2017
Program Committee member, 18th International Conference on Artificial Intelligence in Education (AIED2017), Society

2017 - 2017
Program Committee member, The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)

2016 - 2016
Program Committee member, The Twenty-fifth International Conference on Artificial Intelligence (IJCAI-16), Society

2016 - 2016
Program Committee member, THe Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), Society

Nov. 2015 - Nov. 2015
General Co-Chair, Editor, Second International Workshop on Advanced Methodologies for Bayesian networks (AMBN2015) published by LNAI (Springer))

Jun. 2012 - Jun. 2014
評議員, 人工知能学会, Society

Jun. 2011 - Jun. 2013
研究会委員長, 日本教育工学会, Society

Jun. 2005 - Jun. 2013
理事, 日本教育工学会, Society

Apr. 2012
編集長, 日本行動計量学会, Society

Apr. 2006 - Mar. 2012
副編集長, 日本行動計量学会, Society

Sep. 2011
理事, 日本テスト学会, Society

Sep. 2008 - Aug. 2011
監事, 日本テスト学会, Society

Apr. 2002 - Mar. 2010
編集委員, 教育システム情報学会, Society

Jan. 2008 - Jan. 2010
基本問題研究会幹事, 人工知能学会, Society

Jul. 2007 - Jul. 2007
Conference Representative, Steering Chair, Editor, The 7th IEEE International Conference on Advanced Learning Technologies (ICALT2007), Society

Apr. 2004
評議員, 教育システム情報学会, Society

Apr. 2002
編集委員, 日本教育工学会, Society

Apr. 1995
編集委員, 日本行動計量学会, Society

Jul. 2024
人工知能学会
所要時間におけるDeep-IRTを用いて制限時間を考慮した自動並行テスト構成
全国大会優秀賞（オーガナイズドセッション口頭発表部門）, 石川文弥;渕本壱真;岸田若葉;堤瑛美子;植野真臣

Jun. 2024
電子情報通信学会
学習データの忘却を最適化するHypernetworkを組み込んだDeepIRT
電子情報通信学会論文賞, 堤瑛美子;郭亦鳴;植野真臣

Mar. 2024
日本テスト学会
日本テスト学会賞, 植野 真臣

Aug. 2023
日本行動計量学会
林知己夫賞（功績賞）, 植野 真臣

Sep. 2022
日本テスト学会
等質テスト構成の並列化技術を用いた2段階等質適応型テスト
大会発表賞, 宮澤芳光;渕本壱真;植野真臣

Sep. 2021
教育システム情報学会
学習者のパフォーマンスを高精度に予測するDeep-IRT
大会奨励賞(口頭発表部門), 堤瑛美子;植野真臣
Japan society

Jun. 2021
人工知能学会全国大会
項目反応理論を用いた自動採点モデルの統合手法
全国大会優秀賞(口頭発表部門), 青見樹;堤瑛美子;宇都雅輝;植野真臣
Japan society

2019
JSAI Annual Conference Award
JSAI 2019 Excellence Award (International Session), Yoshimitsu MIYAZAWA;Maomi UENO
Japan society

2019
人工知能学会
Bayesian Knowledge Tracingの一般化としての隠れマルコフIRTモデル
全国大会優秀賞(口頭発表部門), 堤瑛美子;塩野谷周平;宇都雅輝;植野真臣
Japan society

Jun. 2018
電子情報通信学会
複数等質テスト構成における整数計画問題を用いた最大クリーク探索の近似法
電子情報通信学会論文賞, 石井隆稔;赤倉貴子;植野真臣
Official journal, Japan

Dec. 2016
日本テスト学会
項目露出率を最小化する複数等質テスト構成手法
日本テスト学会発表賞

Dec. 2014
日本テスト学会
ピアアセスメントにおける階層ベイズ項目反応モデル
日本テスト学会発表賞

Dec. 2013
日本テスト学会
項目露出率を最小化する複数等質テスト構成手法
日本テスト学会発表賞
Japan society

Nov. 2008
IEEE Computer Society
U.S.A
The 20th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2008) Best Paper Award, Takashi Isozaki;Maomi Ueno
International society, United States

Aug. 2008
The Japan Association for Research on Testing
統合型eテスティング・システムの開発と実践
Japanese Journal for Research on Testing, Best Paper Award, ソンムァン・ポクポン;植野真臣
Japan society

Jun. 2008
ED-MEDIA 2008( World Conference on Educational Multimedia, Hypermedia & Telecommunications)
ED-MEDIA 2008( World Conference on Educational Multimedia, Hypermedia & Telecommunications), Outstanding Paper Award

Oct. 2007
World Conference on E-Learning in Corp., Govt., Health., & Higher Ed
U.S.A
e-Learn2007 Outstanding Paper Award
United States

Aug. 2007
Japanese Society for Enjineering Education
Japanese Society for Enjineering Education, Best Presentation Award

2005
World Conference on E-Learning in Corp., Govt., Health., & Higher Ed
E-LEARN 2005 Outstanding Paper Award

Sep. 2004
The Behaviormetric Society of Japan
The Behaviormetric Society of Japan, Outstanding paper award

Aug. 2004
Japanese Society for information and systems in education
Japanese Society for information and systems in education, outstanding award

2004
World Conference on E-Learning in Corp., Govt., Health., & Higher Ed
E-LEARN 2004 Outstanding Paper Award

1994
日本教育工学会奨励賞

Computerized Adaptive Testing to Balance Exposure Bias and Measurement Accuracy using Zero-suppressed Binary Decision Diagrams
Maomi Ueno; Kazuma Fuchimoto; Wakaba Kishida; Yoshimitsu Miyazawa
Lead, IEEE Access, Institute of Electrical and Electronics Engineers (IEEE), 13, 33883-33903, 19 Feb. 2025, Peer-reviwed
Scientific journal, English

項目難易度制約付き等質適応型テスト
岸田若葉; 渕本壱真; 宮澤芳光; 植野真臣
Last, 電子情報通信学会論文誌 D, Vol. J107–D, No. 10, 506-517, 01 Oct. 2024, Peer-reviwed
Scientific journal, Japanese

Item response theory model highlighting rating scale of a rubric and rater–rubric interaction in objective structured clinical examination
Masaki Uto; Jun Tsuruta; Kouji, Araki; Maomi Ueno
Last, PLOS ONE, 19, 9, e0309887-e0309887, 06 Sep. 2024, Peer-reviwed
Scientific journal

Deep-IRT with temporal convolutional network for comprehensive reflection of student ability history data. Artificial Intelligence in Eduation
Emiko Tsutsumi; Tetsurou Nishio; Maomi Ueno
Last, 25th International Conference on Artificial Intelligence in Education（AIED 2024）, 250-264, Jul. 2024, Peer-reviwed
International conference proceedings, English

学習者の能力の時系列変化を畳み込むTemporal Convolutional Networkを組み込んだDeep-IRT
西尾徹朗; 堤瑛美子; 植野真臣
Last, 電子情報通信学会論文誌 D, Vol. J107–D, No. 3, 98-110, Mar. 2024, Peer-reviwed
Scientific journal, Japanese

深さ優先分枝限定法による目的変数パラメータ数を最小化するベイジアンネットワーク分類器学習
加藤弘也; 菅原聖太; 植野真臣
Last, 電子情報通信学会論文誌 D, J107-D, No.3, 111-122, Mar. 2024, Peer-reviwed
Scientific journal, Japanese

AI・ビッグデータによるアダプティブラーニング
植野真臣
Lead, 人工知能学会, 39, 2, 111-117, Feb. 2024, Peer-reviwed
Japanese

Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters
Shouta Sugahara; Koya Kato; Maomi Ueno
Last, Proceedings of the AAAI Conference on Artificial Intelligence, 38, 18, 20540-20549, Feb. 2024, Peer-reviwed
English

項目露出ペナルティを用いた整数計画法による自動並行テスト構成
渕本壱真; 植野真臣
Last, 統計数理, 72, 1, 43-59, 24 Jan. 2024, Peer-reviwed
Research institution, Japanese

Deep Knowledge Tracing Incorporating a Hypernetwork with Independent Student and Item Networks
Emiko Tsutsumi; Yiming Guo; Ryo Kinoshita; Maomi Ueno
Last, IEEE Transactions on Learning Technologies, 17, 951-965, 2024, Peer-reviwed
Scientific journal, English

Integration of Prediction Scores From Various Automated Essay Scoring Models Using Item Response Theory
Masaki Uto; Itsuki Aomi; Emiko Tsutsumi; Maomi Ueno
Last, IEEE Transactions on Learning Technologies, Institute of Electrical and Electronics Engineers (IEEE), 16, 6, 983-1000, Dec. 2023, Peer-reviwed
Scientific journal

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

Item difficulty constrained uniform adaptive testing
Wakaba Kishida; Kazuma Fuchimoto; Yoshimitsu Miyazawa; Maomi Ueno
Last, 24th International Conference on Artificial Intelligence in Education（AIED 2023）, Jul. 2023, Peer-reviwed
International conference proceedings, English

CBTの最前線
植野 真臣
Lead, 情報処理学会, 64, 5, 1-6, 15 Apr. 2023
Scientific journal, Japanese

高精度能力推定を保証する2段階等質適応型テスト
宮澤芳光; 植野真臣
Last, 電子情報通信学会論文誌 D, J106-D, 1, 34-46, Jan. 2023, Peer-reviwed
Scientific journal, Japanese

BayesFactorを用いたベイジアンネットワークIRTの制約ベース学習
青木健登; 菅原聖太; 植野真臣
Last, 電子情報通信学会論文誌 D, J106-D, 02, 84-95, Jan. 2023, Peer-reviwed
Scientific journal, Japanese

学習データの忘却を最適化するHypernetworkを組み込んだDeepIRT，
堤瑛美子; 郭亦鳴; 植野真臣
Last, 電子情報通信学会論文誌 D, J106-D, 02, 72-83, Jan. 2023, Peer-reviwed
Scientific journal, Japanese

Recursive autonomy identification-based learning of augmented naive Bayes classifiers
Shouta Sugahara; Wakaba Kishida; Koya Kato; Maomi Ueno
Corresponding, Proceedings of Machine Learning Research, PGM2022, Proceedings of Machine Learning Research, 186, 265-276, 05 Oct. 2022, Peer-reviwed
International conference proceedings, English

Zero-suppressed Binary Decision Diagramsを用いた自動テスト構成
渕本壱真; 湊真一; 植野真臣
Last, 人工知能学会論文誌, 人工知能学会論文誌, 37, 5, A-M23_1-A-M23_11, 01 Sep. 2022, Peer-reviwed
Scientific journal, Japanese

項目露出を考慮した整数計画法による等質テスト構成
植野晶; 渕本壱真; 植野 真臣
Last, 電子情報通信学会論文誌 D, 電子情報通信学会, Vol. J105–D, No.8, 485-498, Aug. 2022, Peer-reviwed
Scientific journal, Japanese

Deep knowledge tracing in corporating a hypernetwork with independent student and item networks
Emiko Tsutsumi; Yiming Guo; Maomi Ueno
Last, Proceedings of the 15th International Conference on Educational Data Mining (EDM), -, -, 1-6, 24 Jul. 2022, Peer-reviwed
International conference proceedings, English

分類影響パラメータ数を最小化するベイジアンネットワーク分類器学習
菅原 聖太; 植野 真臣
Last, 電子情報通信学会論文誌D, 電子情報通信学会, Vol.J105, No.1, No.11, 679-690, Jul. 2022, Peer-reviwed
Japanese

Two-Stage Uniform Adaptive Testing to Balance Measurement Accuracy and Item Exposure
Maomi Ueno; Yoshimitsu Miyazawa
Lead, 23rd International Conferenace on Artificial Intelligence in Education (AIED 2022), 626-632, Jun. 2022, Peer-reviwed
International conference proceedings, English

Bayesian Network Model Averaging Classifiers by Subbagging
Shouta Sugahara; Itsuki Aomi; Maomi Ueno
Last, Entropy, MDPI, 24, 05, ---, 23 May 2022, Peer-reviwed, The main idea of this study is to improve the classification accuracy using subbagging, which is modified bagging using random sampling without replacement, to reduce the posterior standard error of each structure in model averaging. Moreover, to guarantee asymptotic consistency, we use the K-best method with the ML score. The experimentally obtained results demonstrate that our proposed method provides more accurate classification than earlier BNC methods and the other state-of-the-art ensemble methods do.
Scientific journal, English

Hybrid Maximum Clique Algorithm Using Parallel Integer Programming for Uniform Test Assembly
Kazuma Fuchimoto; Takatoshi Ishii; Maomi Ueno
IEEE Transactions on Learning Technologies, IEEE, 15, 2, 252-264, 01 Apr. 2022, Peer-reviwed
Scientific journal, English

Exact Learning Augmented Naive Bayes Classifier.
Shouta Sugahara; Maomi Ueno
Last, Entropy, MDPI, 23, 12, ---, 20 Dec. 2021, Peer-reviwed, Earlier studies have shown that classification accuracies of Bayesian networks (BNs) obtained by maximizing the conditional log likelihood (CLL) of a class variable, given the feature variables, were higher than those obtained by maximizing the marginal likelihood (ML). However, differences between the performances of the two scores in the earlier studies may be attributed to the fact that they used approximate learning algorithms, not exact ones. This paper compares the classification accuracies of BNs with approximate learning using CLL to those with exact learning using ML. The results demonstrate that the classification accuracies of BNs obtained by maximizing the ML are higher than those obtained by maximizing the CLL for large data. However, the results also demonstrate that the classification accuracies of exact learning BNs using the ML are much worse than those of other methods when the sample size is small and the class variable has numerous parents. To resolve the problem, we propose an exact learning augmented naive Bayes classifier (ANB)
Scientific journal, English

項目反応理論による小論文自動採点機のモデル平均
青見樹; 堤瑛美子; 宇都雅輝; 植野真臣
Last, 電子情報通信学会論文誌D, J104-D, 11, 01 Nov. 2021, Peer-reviwed
Scientific journal, Japanese

独立な学習者・項目ネットワークをもつDeep-IRT
堤瑛美子; 木下涼; 植野 真臣
Last, 電子情報通信学会論文誌 D, J104, 7, 596-608, 01 Jul. 2021, Peer-reviwed
Scientific journal, Japanese

e-Testing from artificial intelligence approach
Maomi Ueno; Kazuma Fuchimoto; Emiko Tsutsumi
Last, Behaviormetrika, Springer Science and Business Media LLC, 48, 2, 409-424, Jul. 2021, Invited
Scientific journal

AI based e-Testing as a common yardstick for measuring human abilities
Maomi Ueno
18th International Joint Conference on Computer Science and Software Engineering, 1-5, 30 Jun. 2021, Peer-reviwed, Invited
International conference proceedings, English

Deep-IRT with independent student and item networks
Emiko Tsutsumi; Ryo Kinoshita; Maomi Ueno
Last, Proceedings of the 14th International Conference on Educational Data Mining (EDM), 16, 1, 1-10, 01 Jun. 2021, Peer-reviwed
International conference proceedings, English

Integration of Automated Essay Scoring Models using Item Response Theory
Itsuki Aomi; Emiko Tsutsumi; Masaki Uto; Maomi Ueno
International Conference on Artificial Intelligence in Education (AIED), 54-59, Jun. 2021, Peer-reviwed
International conference proceedings, English

Deep Item Response Theory as a Novel Test Theory Based on Deep Learning
Emiko Tsutsumi; Ryo Kinoshita; Maomi Ueno
Electronics, MDPI AG, 10, 9, 1020-1020, 25 Apr. 2021, Peer-reviwed, Item Response Theory (IRT) evaluates, on the same scale, examinees who take different tests. It requires the linkage of examinees’ ability scores as estimated from different tests. However, the IRT linkage techniques assume independently random sampling of examinees’ abilities from a standard normal distribution. Because of this assumption, the linkage not only requires much labor to design, but it also has no guarantee of optimality. To resolve that shortcoming, this study proposes a novel IRT based on deep learning, Deep-IRT, which requires no assumption of randomly sampled examinees’ abilities from a distribution. Experiment results demonstrate that Deep-IRT estimates examinees’ abilities more accurately than the traditional IRT does. Moreover, Deep-IRT can express actual examinees’ ability distributions flexibly, not merely following the standard normal distribution assumed for traditional IRT. Furthermore, the results show that Deep-IRT more accurately predicts examinee responses to unknown items from the examinee’s own past response histories than IRT does.
Scientific journal

Augmented Naive Bayesによる大規模ベイジアンネットワーク分類器学習
菊谷成慎; 菅原聖太; 名取和樹; 植野真臣
電子情報通信学会論文誌D, Vol.J104-D, No.1, 65-81, 01 Jan. 2021, Peer-reviwed
Scientific journal, Japanese

Neural Automated Essay Scoring Incorporating Handcrafted Features
Masaki Uto; Yikuan Xie; Maomi Ueno
Last, Proceedings of the 28th International Conference on Computational Linguistic (COLING), 6077-6088, 08 Dec. 2020, Peer-reviwed
International conference proceedings, English

等質テスト構成における整数計画法を用いた最大クリーク探索の並列化
渕本壱真; 植野真臣
Last, 電子情報通信学会論文誌D, Vol.J103-D, No.12, 881-893, 01 Dec. 2020, Peer-reviwed
Scientific journal, Japanese

Knowledge TracingのためのSliding Window隠れマルコフIRT
堤瑛美子; 木下涼; 植野真臣
Last, 電子情報通信学会論文誌D, Vol.J103-D, No.12, 894-905, 01 Dec. 2020, Peer-reviwed
Scientific journal, Japanese

A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo
Masaki Uto; Maomi Ueno
Behaviormetrika, Springer, 47, 2, 469-496, 24 Jul. 2020, Peer-reviwed
Scientific journal, English

ルーブリック評価における項目反応理論
宇都雅輝; 植野真臣
電子情報通信学会論文誌D, Vol.J103-D, No.05, 459-470, 01 May 2020, Peer-reviwed
Scientific journal, Japanese

ポスト項目反応理論：深層学習によるテスト理論
植野 真臣; 木下 涼
Last, Precision Medicine, 2020年Vol.3 No.5, 5月, 56-62, 05 Apr. 2020, Peer-reviwed
Scientific journal, Japanese

Augmented Naive Bayes制約を持つベイジアンネットワーク分類器の厳密学習
菅原聖太; 植野真臣
電子情報通信学会論文誌D, Vol.J103-D, No.04, 301-313, 01 Apr. 2020, Peer-reviwed
Scientific journal, Japanese

深層学習によるテスト理論：Item Deep Response Theory
木下涼; 植野真臣
電子情報通信学会論文誌D, Vol.J103-D, No.04, 314-329, 01 Apr. 2020, Peer-reviwed
Scientific journal, Japanese

Group optimization to maximize peer assessment accuracy using item response theory and integer programming
Masaki Uto; Duc-Thien Nguyen; Maomi Ueno
IEEE Transactions on Learning Technologies,IEEE computer Society, 13, 1, 91-106, 21 Mar. 2020, Peer-reviwed
Scientific journal, English

アンサンブル学習によるモデル平均ベイジアンネットワーク分類器
青見樹; 菅原聖太; 植野真臣
電子情報通信学会論文誌D, J103-D, 03, 183-193, 01 Mar. 2020, Peer-reviwed
Scientific journal, Japanese

Learning huge Bayesian network structures using the transitivity
Kazunori Honda; Kazuki Natori; Shota Sugahara; Takashi Isozaki; Maomi Ueno
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS, D, 12, 796-811, Dec. 2019, Peer-reviwed
Scientific journal, Japanese

Uniform adaptive testing using maximum clique algorithm
Maomi Ueno; Yoshimitsu Miyazawa
International Conference on Artificial Intelligence in Education (AIED), 482-493, 21 Jun. 2019, Peer-reviwed
International conference proceedings, English

ダイナミックアセスメントのための隠れマルコフIRTモデル
堤瑛美子; 宇都雅輝; 植野真臣
電子情報通信学会論文誌 D, J102-D, 2, 79-92, 01 Feb. 2019, Peer-reviwed
Scientific journal, Japanese

Computerized Adaptive Testing Method Using Integer Programming to Minimize Item Exposure.
Yoshimitsu Miyazawa; Maomi Ueno
Advances in Artificial Intelligence, 105-113, 2019, Peer-reviwed
International conference proceedings, English

IRT-Based Adaptive Hints to Scaffold Learning in Programming
Maomi Ueno; Yoshimitsu Miyazawa
IEEE Transactions on Learning Technologies, IEEE computer Society, IEEE computer Society, 11, 4, 415-428, 01 Oct. 2018, Peer-reviwed
Scientific journal, English

Exact learning augmented naïve Bayes classifier
Shouta Sugahara; Masaki Uto; Maomi Ueno
Proceedings of Machine Learning Research (International Conference on Probabilistic Graphical Models), 72, 439-450, 11 Sep. 2018, Peer-reviwed
International conference proceedings, English

Item Response Theory Without Restriction of Equal Interval Scale for Rater’s Score
Masaki Uto; Maomi Ueno
International Conference on Artificial Intelligence in Education (AIED), 363-368, 30 Jun. 2018, Peer-reviwed
International conference proceedings, English

測定精度の偏り軽減のための等質適応型テストの提案
宮澤芳光; 宇都雅輝; 石井隆稔; 植野真臣
電子情報通信学会論文誌D, J101-D, 6, 909-920, 01 Jun. 2018, Peer-reviwed
Scientific journal, Japanese

Bayes factorを用いたRAIアルゴリズムによる大規模ベイジアンネットワーク学習
名取和樹; 宇都雅輝; 植野真臣
電子情報通信学会論文誌 D, J101-D, 5, 754-768, 01 May 2018, Peer-reviwed
Scientific journal, Japanese

Empirical comparison of item response theory models with rater's parameters
Masaki Uto; Maomi Ueno
Heliyon, Elsevier Ltd, 4, 5, 1-32, 01 May 2018, Peer-reviwed
Scientific journal, English

ピアアセスメントにおける項目反応理論を用いたグループ構成最適化
グエン ドク ティエン; 宇都 雅輝; 植野 真臣
電子情報通信学会論文誌 D, J101-D, 2, 431-445, 01 Feb. 2018, Peer-reviwed
Scientific journal, Japanese

ピアアセスメントにおける異質評価者に頑健な項目反応理論
宇都雅輝; 植野真臣
電子情報通信学会論文誌 D, J101-D, 1, 211-224, 01 Jan. 2018, Peer-reviwed
Scientific journal, Japanese

Curriculum development for Educational Technology based on comparisons of course syllabi resources using lexical analysis
Minoru Nakayama; Katsuaki Suzuki; Chiharu Kogo; Maomi Ueno
EAI Endorsed Transactions on e-Learning, EAI Endorsed Transactions on e-Learning, 4, 16, 1-8, 19 Dec. 2017, Peer-reviwed
Scientific journal, English

Consistent Learning Bayesian Networks with Thousands of Variables
Kazuki Natori; Masaki Uto; Maomi Ueno
The 3rd Workshop on Advanced Methodologies for Bayesian Networks (AMBN), 73, 57-68, 20 Sep. 2017, Peer-reviwed
International conference proceedings, English

Classification of Japanese Graduate Schools: In terms of educational practices and the grown globalization competencies by the policies
Taiyo Utsuhara; Masaki Uto; Asana Ishihara; Atsushi Yoshikawa; Maomi Ueno
International Federation of Classification Societies, CN02, 08 Aug. 2017, Peer-reviwed
International conference proceedings, English

Features of Globalization in Japanese Graduate Schools
Taiyo Utsuhara; Masaki Uto; Asana Ishihara; Koichi Ota; Ayako Hirano; Atsushi Yoshikawa; Maomi Ueno
International Conference on Education, 392-1-392-10, 13 Feb. 2017, Peer-reviwed
International conference proceedings, English

複数等質テスト構成における整数計画問題を用いた最大クリーク探索の近似法
石井隆稔; 赤倉貴子; 植野真臣
電子情報通信学会論文誌Ｄ, J100-D, 1, 47-59, 20 Jan. 2017, Peer-reviwed
Scientific journal, Japanese

An extended depth-first search algorithm for optimal triangulation of Bayesian networks
Chao Li; Maomi Ueno
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 80, C, 294-312, Jan. 2017, Peer-reviwed
Scientific journal, English

Algorithm for uniform test assembly using a maximum clique problem and integer programming
Takatoshi Ishii; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 10331, 102-112, 2017, Peer-reviwed
International conference proceedings, English

Group optimization to maximize peer assessment accuracy using item response theory
Masaki Uto; Nguyen Duc Thien; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 10331, 393-405, 2017, Peer-reviwed
International conference proceedings, English

Item Response Theory for Peer Assessment
Masaki Uto; Maomi Ueno
IEEE Transactions on Learning Technologies, IEEE computer Society, 9, 2, 157-170, 22 Jun. 2016, Peer-reviwed
Scientific journal, English

ビッグデータとその解析手法
福島綾一; 植野真臣
日本情報経営学会誌, Japan Society for Information and Management, 36, 4, 18-28, 20 Jun. 2016, Peer-reviwed, Invited, This article introduces and reviews recent analyses methods on big data. We first introduce several kinds of definitions of big data based on Volume，Variety，Velocity, and Value extracted from data. Next, we describe that big data can be classified into three types – 1. various kinds of data with very large volume or producing very fast, 2. sparse data and 3. universal data –. Then we derive three important factors for utilizing big data – big data technologies, visualization and techniques for analyzing big data – and introduce the details of each factor corresponding to that three types of big data.
Scientific journal, Japanese

パフォーマンス評価のための項目反応モデルの比較と展望
宇都雅輝; 植野真臣
日本テスト学会誌, 12, 1, 55-75, 01 Apr. 2016, Peer-reviwed
Scientific journal, Japanese

Latent Dirichlet Allocatonを用いたレポート推薦システム
加藤嘉浩; 石井隆稔; 宮澤芳光; 植野真臣
電子情報通信学会論文誌, J99-D, 2, 152-164, 01 Feb. 2016, Peer-reviwed
Scientific journal, Japanese

Reliable Peer Assessment for Team-project-based Learning using Item Response Theory
Thien Nguyen; Masaki Uto; Yu Abe; Maomi Ueno
International Conference on Computers in Education (ICCE), 144-153, 02 Dec. 2015, Peer-reviwed
International conference proceedings, English

構成主義的学習におけるルーブリックの活用方法が学習者に与える影響分析 －目標志向性，学習観，動機づけ，学習方略，学習課題成績に着目して－
山本美紀; 植野真臣
日本教育工学会論文誌, 39, 2, 67-81, 01 Dec. 2015, Peer-reviwed
Scientific journal, Japanese

A note on judgement model of high risk companies by analyzing corporate registration's data
Roichi Fukushima; Maomi Ueno
Intelligence Management, The Japan society of competitive intelligence, 6, 1, 33-42, 01 Sep. 2015, Peer-reviwed
Scientific journal, Japanese

Support of learning from the others
Maomi Ueno
Journal of the Japanese society for artificial intelligence, the Japanese society for artificial intelligence, 30, 4, 469-472, 01 Jul. 2015, Peer-reviwed, Invited
Scientific journal, Japanese

A review of uniform test assembly methods for e-testing
Takatoshi Ishii; Maomi Ueno
Japanese Journal for Research on Testing, 日本テスト学会, 11, 1, 131-149, 29 Jun. 2015, Peer-reviwed
Scientific journal, Japanese

Sensorless Rotor Position Detection for SRMs with Drive Current-isolated Signaling System
Kenji Yamamoto; Hisashi Takahashi; Nobumasa Ushiro; Koki Shirakawa; Maomi Ueno
IEEJ Transactiond on Industry Applications, 135, 5, 521-530, 01 Apr. 2015, Peer-reviwed
Scientific journal, Japanese

Item Response Theory with assessors' lower order parameters of peer assessment
Masaki Uto; Maomi Ueno
電子情報通信学会論文誌 D. Vol.98-D, J98-D, 1, 3-16, 06 Jan. 2015, Peer-reviwed
Scientific journal, Japanese

Mobile testing system optimizing test information and movement distance
Yoshimitsu Miyazawa; Maomi Ueno
電子情報通信学会論文誌 D. Vol.98-D, J98-D, 1, 30-41, 06 Jan. 2015, Peer-reviwed
Scientific journal, Japanese

Scaffolding system that provides adaptive hints using item response theory
Maomi Ueno; Junya Matsuo
電子情報通信学会論文誌 D. Vol.98-D, J98-D, 1, 17-29, 06 Jan. 2015, Peer-reviwed
Scientific journal, Japanese

SNS Messages Recommendation for Learning Motivation
Sebastien Louvigne; Yoshihiro Kato; Neil Rubens; Maomi Ueno
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 9112, 237-246, 2015, Peer-reviwed
International conference proceedings, English

Item Response Model with Lower Order Parameters for Peer Assessment
Masaki Uto; Maomi Ueno
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 9112, 800-803, 2015, Peer-reviwed
International conference proceedings, English

Clique Algorithm to Minimize Item Exposure for Uniform Test Forms Assembly
Takatoshi Ishii; Maomi Ueno
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 9112, 638-641, 2015, Peer-reviwed
International conference proceedings, English

Probability Based Scaffolding System with Fading
Maomi Ueno; Yoshimitsu Miyasawa
ARTIFICIAL INTELLIGENCE IN EDUCATION, AIED 2015, 9112, 492-503, 2015, Peer-reviwed
International conference proceedings, English

Academic Writing Support System using Bayesian Networks
Masaki Uto; Maomi Ueno
15TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT 2015), 385-387, 2015, Peer-reviwed
International conference proceedings, English

Constraint-based learning Bayesian networks using Bayes factor
Kazuki Natori; Masaki Uto; Yu Nishiyama; Shuichi Kawano; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 9505, 15-31, 2015, Peer-reviwed
International conference proceedings, English

A fast clique maintenance algorithm for optimal triangulation of Bayesian networks
Chao Li; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 9505, 152-167, 2015, Peer-reviwed
International conference proceedings, English

ePortfolio system using past learners' history data
Maomi Ueno
Journal of Japan Socierty of Information and Knowledge, Japan Society of Information and Knoweledge, 24, 4, 414-423, 04 Dec. 2014, Peer-reviwed, Invited
Scientific journal, Japanese

Maximum Clique Algorithm for Uniform Test Forms Assembly and its approximation
Takatoshi Ishii; Pokpong Songmuang; Maomi Ueno
IEEE Transactions on Learning Technologies, IEEE computer Society, IEEE computer Society, 7, 1, 1-13, 01 Mar. 2014, Peer-reviwed
Scientific journal, English

Mximum Clique Algorithm for uniform test assembly
Takatoshi Ishii; Pokpong Sogmuang; Maomi Ueno
電子情報通信学会論文誌 D. Vol.J97-D No2. pp.270-280, The Institute of Electronics, Information and Communication Engineers, Vol.J97-D, 2, 270-280, 01 Feb. 2014, Peer-reviwed, 本論文では,複数等質テストを自動構成する新しい手法を提案する.複数等質テストとは,それぞれのテストに含まれるテスト項目は異なるが,統計的な性質(例えば,得点分布や項目反応理論に基づく情報量等)が等しいテスト群である.本手法の特徴は,与えられたアイテムバンクから最大数の複数等質テストを構成できることである.具体的には,複数等質テスト構成を最大クリーク問題として解き,等質テスト間に項目の重複を許した条件でもテスト構成が可能な手法を提案する.これにより構成可能なテスト数を,重複を許さない場合に比べ,大きく増加させることができる.しかし,提案手法は計算コストが高く,大規模なアイテムバンクでは計算を打ち切る必要がある.本論文ではシミュレーション及び実データを用いた実験を行い,計算を打ち切った場合でも,本手法が他手法より多くのテストを構成できることを示した.
Scientific journal, Japanese

Maximum Clique Algorithm and Its Approximation for Uniform Test Form Assembly
Takatoshi Ishii; Pokpong Songmuang; Maomi Ueno
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES, 7, 1, 83-95, Jan. 2014, Peer-reviwed
Scientific journal, English

Goal-based messages Recommendation utilizing Latent Dirichlet Allocation
Ebastien Louvigne; Yoshihiro Kato; Neil Rubens; Maomi Ueno
2014 14TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES (ICALT), 464-468, 2014, Peer-reviwed
International conference proceedings, English

Toulmin Model based Argument Elaboration Support System using Bayesian Net- work Representation
Masaki Uto; Maomi Ueno
IEICE, The Institute of Electronics, Information and Communication Engineers, J96-D, 4, 998-1011, Apr. 2013, Peer-reviwed, 本論文では,アカデミックライティングにおける論証の推敲を支援するシステムを開発する.従来の論証推敲支援システムでは,論証の規範モデルとして知られるToulminモデルにユーザの論証を当てはめ可視化する支援を行っていることが多い.しかし,論証の主目的である「主張」の正当化のためには,Toulminモデルへの当てはまりの良さよりも,文章間の因果の強さ,すなわち「論証の強さ」を重視した論証の推敲が重要である.論証の推敲では,論証構成が複雑になったとき,以下の問題が生じると考えられる.1.「論証の強さ」を全ての文章間について評価することが困難である.2.論証中の各文章がどの程度正当化できているかの推定が難しい.3.「主張」の正当化に対して各文章がどのように影響しているかを把握することが困難である.これらの問題を解決するために,本論文では,Toulminモデルのベイジアンネットワーク表現を用いて,1.論証の強さ,2.文章の正当性,3.主張への影響度,という三つの指標を算出し,その値に応じて論証改訂のためのアドバイスをフィードバックする論証推敲支援システムを開発する.
Scientific journal, Japanese

Mobile testing for authentic assessment in a feld:Evaluation from Actual Performances
Yoshimitsu Miyasawa; Maomi Ueno
IEEE Region 10 Humanitarian Technology Conference, 232-237, 2013, Peer-reviwed
International conference proceedings, English

Adaptive testing based on bayesian decision theory
Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7926, 712-716, 2013, Peer-reviwed
International conference proceedings, English

Maximum clique algorithm for uniform test forms assembly
Takatoshi Ishii; Pokpong Songmuang; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7926, 451-462, 2013, Peer-reviwed
International conference proceedings, English

Mobile testing for authentic assessment in the field
Yoshimitsu Miyasawa; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7926, 619-623, 2013, Peer-reviwed
International conference proceedings, English

A Depth-First Search Algorithm for Optimal Triangulation of Bayesian Network
Chao Li; Maomi Ueno
Proceedings of The Sixth European Workshop on Probabilistic Graphical Models(PGM), 187-194, Sep. 2012
International conference proceedings, English

Non-Informative Dirichlet Score for learning Bayesian networks
Maomi Ueno; Masaki Uto
Proceedings of The Sixth European Workshop on Probabilistic Graphical Models(PGM), 331-338, Sep. 2012, Peer-reviwed
International conference proceedings, English

Mobile sightseeing and learning navigation system using adaptive testing
Yoshimitsu Miyazawa; Maomi Ueno
Transactions of Japanese Society for Information and Systems in Education, 教育システム情報学会事務局, 29, 2, 110-123, Apr. 2012, Peer-reviwed
Scientific journal, Japanese

ePortfolio which facilitates learning from others
Maomi Ueno; Masaki Uto
Japanese journal of educational technology, Japan Society for Educational Technology, 35, 3, 169-182, Dec. 2011, Peer-reviwed, This study proposes an ePortfolio system encouraging students to learn from others. The system (1) provides structured individual ePortfolios and hyperlinks that enable students to quickly locate useful information created by others in various ways, (2) features user-friendly, intelligent search capabilities making it easy to search for keywords and identify other bright students (based on past reports, test scores, peer assessment, and so on), and (3) supports an array of assessment techniques at all levels-tests, peer assessments, self assessments, instructor-designated best practices, ability to input comments from other students, hyperlinks-that not only encourage students to reflect upon their own learning but can be used to quickly identify the best students who consistently do excellent work. Actual trial use of the system demonstrates that Samurai-folio does indeed promote learning from others, and supports sustainability of learning and deeper robust acquisition of knowledge; not superficial learning based on memorization.
Scientific journal, Japanese

Article structure construction support system by Bayes code
Masaki Uto; Maomi Ueno
IEICE Transaction on Information and Systems, The Institute of Electronics, Information and Communication Engineers, J94-D, 12, 2069-2081, Dec. 2011, Peer-reviwed, 本研究では,妥当かつ多様な論文構成の構築を支援するシステムの開発と評価を行う.ここでは,「論文構成」を情報理論における情報源からの出力符号系列とみなしたメタファとしてとらえ,論文構成の構築過程を定式化する.具体的には,過去の優良論文100件の論文構成を論文要素カテゴリーの系列データとし,それがm重マルコフ情報源に従うと仮定する.多重度の推定法として,情報論的アプローチでは,ベイズ符号語長(Bayes code length)最小化による推定法が高精度であると知られている.しかし,本論文で扱うようなデータ長の短いデータから学習する場合,多重度の増加に伴いベイズ符号語長が単調減少し,多重度を正しく推定できないことがある.そこで,本研究では,ベイズ符号語長が単調減少する場合の推定補正法を提案し,過去の優良論文100件から予測精度の高いm重マルコフ情報源を推定する.更に,推定されたマルコフ情報源に基づき論文構成の構築過程を逐次的にナビゲーションするシステムを開発する.最後に,評価実験を行い,補正手法及び提案システムの有効性を評価する.
Scientific journal, Japanese

Item latent structure analysis by marginalizing latent variable
Takamitsu Hashimoto; Maomi Ueno
Japanese journal of educational technology, Japan Society for Educational Technology, 35, 3, 205-215, Dec. 2011, Peer-reviwed, Item Relational Structure (IRS) analysis and fuzzy graphing are known as popular methods to express relations among test items. These methods can estimate the item structure from test data through easy calculation. However, when two items are affected strongly by the latent ability variable, relations can be detected incorrectly because of the relation through the latent variable. This paper introduces Item Latent Structure (ILS) analysis, which uses the Latent Conditional Independence (LCI) test, to assess the conditional independence between two items given a latent variable. After simulation and application to actual data, results demonstrate that ILS analysis can detect conditional independence correctly given a latent ability variable.
Scientific journal, Japanese

Effect analysis of e-Learning that uses tablet PC
Masahiro Ando; Maomi Ueno
Japan journal of educational technology, Japan Society for Educational Technology, 35, 2, 109-124, Nov. 2011, Peer-reviwed, This paper discusses the effect of using tablet PCs in e-learning. We carried out an analysis based on the "dual channel model," which models the information-processing capabilities of humans. More specifically, we provided learners with paper media, keyboards, pen tablets, and tablet PCs to be used as input devices for annotations during e-learning, measured the gaze point of each learner using an eye-mark recorder, and analyzed the performance of each device by performing memory and comprehension tests, using questionnaires, and evaluating the note-taking activity. As a result, we found that the use of tablet PCs in e-learning (1) reduces the extraneous cognitive load imposed by making annotations, (2) makes it easier to gaze at the content in synchronization with the narration, (3) increases learners' comprehension and memory retention abilities, and (4) enables efficient note taking, thus increasing the accuracy of the notes as learning aids.
Scientific journal, Japanese

Learning Community using Social Network Service
Maomi Ueno; Masaki Uto
Web Based Communities and Social Media 2011 Conference (IADIS), 109-119, 26 Jul. 2011, Peer-reviwed
International conference proceedings, English

Robust learning Bayesian networks for prior belief
Maomi Ueno
Proceedings of the Twenty-Seventh Conference of Uncertainty in Artificial Intelligence(2011), 698-707, Jul. 2011, Peer-reviwed
International conference proceedings, English

Latent Conditional Independence Test Using Bayesian Network Item Response Theory
Takamitsu Hashimoto; Maomi Ueno
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, E94D, 4, 743-753, Apr. 2011, Peer-reviwed
Scientific journal, English

Detection of mutually dependent test items using the LCI test
Takamitsu Hashimoto; Maomi Ueno
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6797, 196-209, 2011, Peer-reviwed
International conference proceedings, English

Bees algorithm for construction of multiple test forms in E-testing
Pokpong Songmuang; Maomi Ueno
IEEE Transactions on Learning Technologies, 4, 3, 209-221, 2011, Peer-reviwed
Scientific journal, English

Learning networks determined by the ratio of prior and data
Maomi Ueno
Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (2010), 598-605, Jul. 2010, Peer-reviwed
International conference proceedings, English

Web database system using webcam for children's problem behavior in special needs education
Masahito Nagamori; Masaki Nagasawa; Maomi Ueno
Japanese journal of educational technology, Japan Society for Educational Technology, 34, 1, 1-12, Jan. 2010, In this research we developed a special needs education case database system to store and share the data of children's problem behavior. The system uses webcams to record children's problem behavior in the classroom. The main benefits of our system are as follows: (1) The webcam can store the video to a server from 20 seconds before the starting point when the teacher starts recording with a wireless mouse during the class. This makes it possible for a teacher to capture the children's problem behaviors that usually occur suddenly. (2) Teachers can clearly describe the children's problem behaviors in electronic educational records using the video recordings of the webcam even when the memory is unclear. (3) The system is designed for up to 4 webcams. This allows recording the children's problem behaviors from multiple perspectives. This also allows teachers to clearly describe in electronic educational records the problem behaviors, even the ones that are difficult to capture from single perspective. We have verified the effectiveness of this system through experiment and utilization in classroom trials.
Scientific journal, Japanese

Analysis of the advantages of using tablet PC in e-learning
Masahiro Ando; Maomi Ueno
Proceedings - 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010, 122-124, 2010, Peer-reviwed
International conference proceedings, English

Computerized adaptive testing based on decision tree
Maomi Ueno; Pokpong Songmuang
Proceedings - 10th IEEE International Conference on Advanced Learning Technologies, ICALT 2010, 191-193, 2010, Peer-reviwed
International conference proceedings, English

"DATA TEMPERATURE" IN MINIMUM FREE ENERGIES FOR PARAMETER LEARNING OF BAYESIAN NETWORKS
Takashi Isozaki; Noriji Kato; Maomi Ueno
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 18, 5, 653-671, Oct. 2009, Peer-reviwed
Scientific journal, English

Intelligent LMS with an agent that learns from log data
Maomi Ueno
The journal of Inforamtion and Systems in Education, 7, 1, 3-14, May 2009, Peer-reviwed
Scientific journal, English

Current educational technology research trends in Japan
Minoru Nakayama; Maomi Ueno
ETR&D-EDUCATIONAL TECHNOLOGY RESEARCH AND DEVELOPMENT, 57, 2, 271-285, Apr. 2009, Peer-reviwed
Scientific journal, English

Minimum Free Energy Principle for Constraint-Based Learning Bayesian Networks
Takashi Isozaki; Maomi Ueno
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I, 5781, 612-627, 2009
International conference proceedings, English

Development of prediction system of score and time in e-testing
Pokpong Songmuang; Maomi Ueno
The IEICE Transactions of Information and Systems, The Institute of Electronics, Information and Communication Engineers, J91-D, 9, 2225-2235, Sep. 2008, Peer-reviwed, 本論文では,eテスティングにおける得点・時間予測システムを提案する.具体的には,(1)ベータ二項分布,混合二項分布を組み合わせて拡張した混合ベータ二項分布,(2)項目反応理論(ラッシュモデル,2パラメータロジスティックモデル)を用いたテスト得点分布を提案し,従来に用いられてきた得点分布,二項分布,混合二項分布,ベータ二項分布,切断指数分布と予測精度を比較する.また,テスト予測所要時間モデルとして,これまで提案されてきた所要時間分布(正規分布,対数正規分布,拡張ガンマ分布,ワイブル分布)による予測精度の比較を行う.結果,テスト得点予測モデルとして項目反応理論(2パラメータロジスティックモデル),テスト所要時間予測モデルとして拡張ガンマ分布モデルが最も良い予測精度を示した.以上の結果を用いて,テスト構成過程における構成されたテストの予測得点分布,予測所要時間分布の状態を逐次可視化するウェブベーステスト構成支援システムを開発し,実データを用いた評価よりその有効性を示した.
Scientific journal, Japanese

Learning likelihood-equivalence Bayesian networks using an empirical Bayesian approach
Maomi Ueno
Behaviormetrika, The Behaviormetric Society of Japan, 35, 2, 115-135, Jul. 2008, Peer-reviwed, Many studies on learning Bayesian networks have used the Dirichlet prior score metric (DPSM). Although they assume different optimum hyper-parameter values for DPSM, few studies have focused on selection of optimum hyper-parameter values. Analyses of DPSM hyper-parameters for learning Bayesian networks are presented here along with the following results: 1. DPSM has a strong consistency for any hyper-parameter values. That is, the score metric DPSM, uniform prior score metric (UPSM), likelihood-equivalence Bayesian Dirichlet score metric (BDe), and minimum description length (MDL) asymptotically converge to the same results. 2. The optimal hyper-parameter values are affected by the true network structure and the number of data. 3. Contrary to Yang and Chang (2002)'s results, BDe based on likelihood equivalence is a theoretically and actually reasonable score metric, if the optimum hyper-parameter values can be found. Using these results, this paper proposes a new learning Bayesian network method based on BDeu that uses the empirical Bayesian approach. The unique features of this method are: 1. It is able to reflect a user's prior knowledge. 2. It has both the strong consistency and likelihood equivalence properties. 3. It finds the optimum hyper-parameter value of BDeu to maximize predictive efficiency, by adapting to domain and data size. In addition, this paper presents some numerical examples using the proposed method that demonstrate the effectiveness of the proposed method.
Scientific journal, English

Collaborative filtering for massive datasets based on Bayesian networks
Maomi Ueno; Takahiro Yamazaki
Behaviormetrika, The Behaviormetric Society of Japan, 35, 2, 137-158, Jul. 2008, Peer-reviwed, This paper proposes a collaborative filtering method for massive datasets that is based on Bayesian networks. We first compare the prediction accuracy of four scoring-based learning Bayesian networks algorithms (AIC, MDL, UPSM, and BDeu) and two conditional-independence-based (CI-based) learning Bayesian networks algorithms (MWST, and Polytree-MWST) using actual massive datasets. The results show that (1) for large networks, the scoring-based algorithms have lower prediction accuracy than the CI-based algorithms and (2) when the scoring-based algorithms use a greedy search to learn a large network, algorithms which make a lot of arcs tend to have less prediction accuracy than those that make fewer arcs. Next, we propose a learning algorithm based on MWST for collaborative filtering of massive datasets. The proposed algorithm employs a traditional data mining technique, the "a priori" algorithm, to quickly calculate the amount of mutual information, which is needed in MWST, from massive datasets. We compare the original MWST algorithm and the proposed algorithm on actual data, and the comparison shows the effectiveness of the proposed algorithm.
Scientific journal, English

Effect analysis of pointer presentation on multimedia e-learning materials based on dual channel model
Masahiro Ando; Maomi Ueno
Japan Journal of Educational Technology, Japan Society for Educational Technology, 32, 1, 43-56, Jun. 2008, Peer-reviwed, In e-learning area, the content development method is one of the most important research topics. This paper assumes a presentation method of visual contents (a text, a still image) in synchronization with sound content (narration) and a pointer to make the efficiency of the resource allocation of cognitive memory capacity increase, and make the transmitted amount of information increase, based on a human's cognitive-information-processing model "Dual Channel Model." Under various contents presentation environment {(l)Narration, (2)Text (with narration / without narration), (3)Still images, (4)Still images + text (with narration / without narration), (5)Video, and (6)Video + text} in e-learning, we performed some control experiments (measure the point of fixation for e-learning students by an eye mark recorder, memorization and a contents understanding test and the questionnaire) with or without pointer. The results showed that the pointer does not affect learners' surface knowledge acquisition but affects learners' deep knowledge acquisition.
Scientific journal, Japanese

Development and Practice of an Integrative e-Testing System
Pokpong Songmuang; Maomi Ueno
Japanese Journal for Research on Testing, 4, 1, 54-64, May 2008, Peer-reviwed
Scientific journal, Japanese

Practice of Distance Education for two or more Classes that uses Mobile Phone
NAGAMORI Masahito; ANDO Masahiro; POKPONG Songmuang; UENO Maomi
Journal of Jsee, Japanese Society for Engineering Education, 56, 2, 14-19, 20 Mar. 2008, It is known that there are the following problems in distance education for two or more classes. 1) The reaction of the learner in the remote place is not understood easily. 2) It sometimes confuses the teacher which class he/she should observe. 3) It is difficult to realize an interactive lecture. This paper proposes a response analyzer system using mobile phones in order to solve these problems. The following item exists as an advantage of response analyzer system using mobile phones. 1) Use in the general classroom without equipment is possible. 2) The introduction of the system is cheap. 3) The operation must be easy because the learner is usually using the mobile phone. The system in the server gathers the learners′ responses in distance places and shows the teacher various data-analysis results on line. Therefore, the teacher can grasp the learners′ states on-line, and can make a teaching decision (lecture pace, class which must be seen) optimally.
Japanese

Item Response Theory with Assesors' parameters of Peer Assessment
Maomi Ueno; Pokpong Songmuang; Toshio Okamoto; Keizo Nagaoka
The IEICE Transaction on Information and Systems, The Institute of Electronics, Information and Communication Engineers, J91-D, 2, 377-388, Feb. 2008, Peer-reviwed, 近年,真正な評価活動の一つとして学習者同士による成果の相互評価,ピアアセスメント(Peer Assessment)と呼ばれる評価手法が注目されてきている.本論文では,ピアアセスメントにおける項目反応理論を提案する.具体的には,ピアアセスメントにおけるm段階評価反応に対応し,項目反応理論の一つである段階反応モデル(Graded Item Response model)に評価者の評価基準パラメータを加えたモデルである.その利点は,以下のとおりである.1.各評価者のもつ評価基準が互いに異なる場合にも,同一尺度上での評価を行うことができる.2.各評価者の特性を考慮した学習者の評価を行うことができ,結果として信頼性の高い評価を行うことができる.3.欠測値をもつデータからモデルの各パラメータを容易に推定できる.4.上記1〜3の結果として,提案手法により学習者の成果評価の推定精度の向上が可能となる.5.推定されたパラメータより,課題,評価者の特性評価が行える.また,本モデルを用いて,1.評価者の評価基準の厳しさ,2.評価者の評価基準の識別力,を導出し,評価者の分析に用いることを提案している.実際に提案手法を実データに適用することにより,本モデルの有用性を示した.
Scientific journal, Japanese

Cognitive load reduction on multimedia e-learning materials
Masahiro Ando; Maomi Ueno
8TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 268-272, 2008, Peer-reviwed
International conference proceedings, English

Item response theory for peer assessment
Maomi Ueno; Toshio Okamoto
8TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 554-558, 2008, Peer-reviwed
International conference proceedings, English

System for online detection of aberrant responses in e-testing
Maomi Ueno; Toshio Okamoto
8TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 824-828, 2008, Peer-reviwed
International conference proceedings, English

Minimum Free Energies with "Data Temperature" for Parameter Learning of Bayesian Networks
Takashi Isozaki; Noriji Kato; Maomi Ueno
20TH IEEE INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, VOL 1, PROCEEDINGS, 371-378, 2008
International conference proceedings, English

Proceedings - The 7th IEEE International Conference on Advanced Learning Technologies ICALT, 2007: Preface
J. Michael Spector; Demetrios G. Sampson; Toshio Okamoto; Kinshuk; Stefano A. Cerri; Maomi Ueno; Akihiro Kashihara
Proceedings - The 7th IEEE International Conference on Advanced Learning Technologies, ICALT 2007, xx-xxi, 2007, Peer-reviwed
International conference proceedings, English

E-testing construction support system with some prediction tools
Pokpong Songmuang; Masahiro Ando; Masahito Nagamori; Maomi Ueno; Toshio Okamoto
7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 437-+, 2007, Peer-reviwed
International conference proceedings, English

Collaborative e-learning among teachers using a web database in special support education
Masahito Nagamori; Msahiro Ando; Masaki Nagasawa; Pokpong Songmuang; Maomi Ueno
7th IEEE International Conference on Advanced Learning Technologies, Proceedings, 328-329, 2007, Peer-reviwed
International conference proceedings, English

Bayesian agent in e-learning
Maomi Ueno; Toshio Okamoto
7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 282-+, 2007, Peer-reviwed
International conference proceedings, English

An analysis using eye-mark recorder of the effectiveness of presentation methods for e-learning
Masahiro Ando; Masahito Nagamori; Pokpong Songmuang; Maomi Ueno; Toshio Okamoto
7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 183-+, 2007, Peer-reviwed
International conference proceedings, English

Evaluating learners' knowledge-structure using Bayesian networks
Yasuko Namatame; Maomi Ueno
7TH IEEE INTERNATIONAL CONFERENCE ON ADVANCED LEARNING TECHNOLOGIES, PROCEEDINGS, 439-+, 2007, Peer-reviwed
International conference proceedings, English

Collaborative e-test construction - Using predicted response-time and score distributions to improve reliability
Pokpong Songmuang; Maomi Ueno
KNOWLEDGE MANAGEMENT FOR EDUCATIONAL INNOVATION, 230, 195-+, 2007, Peer-reviwed
International conference proceedings, English

Online Outlier detection for e-learning time data
Maomi Ueno
The IEICE Transactions on Information and Systems, The Institute of Electronics, Information and Communication Engineers, J90-D, 1, 40-51, Jan. 2007, Peer-reviwed, 本論文では,eラーニングコンテンツに対する学習所要時間データを用い,オンラインで学習者の異常学習プロセスを検知する手法を提案する.具体的には,学習所要時間データ系列{x_1,x_2,…,x_n}を所与として,新たなデータx_の出現する確率分布をベイズ予測分布を用いて導き,新たなデータx_の異質性を検定するというもので,以下のような利点をもっている.(1)数学的に導出された異常値検出モデルは,異常値検出のために必要とされるデータ数を理論的に反映しているために,判断材料となるデータが少ない時点では異常値検出の基準を緩め,データ数が増すに応じて基準を厳しくしていく特性をもつ.そのために,データ数が少ない時点で正常値を異常値と判断し,その後の検出に影響することを避けることができる.(2)過去の学習履歴データを用いて,各コンテンツの学習所要時間の平均,標準偏差を逐次計算し,それらを用いて学習者の各コンテンツへの学習時間データを標準化したデータ系列より,異常値検出を行うアルゴリズムを提案している.コンテンッ間で標準化されたデータを対象としたモデルを提案することにより,時系列データに対するコンテンツの特性(平均所要時間,標準偏差)の差の影響を除去できる.(3)事前分布のハイパパラメータを変化させることにより,異常値判定基準が様々な統計検定に変化し,状況に応じた検定法を選択することができる.更に,実際にこれらの原理を組み込んだLMS(Learning Management System)を開発し,本手法について,1.シミュレーション,2.学習者からの異常学習プロセスの自己申告との一致性評価を行い,その有効性を示す.
Scientific journal, Japanese

URL

Effectiveness of Collaborative e-Test Construction
Pokpong Songmuang; Maomi Ueno; Toshio Okamoto
Proc. of Advanced Learning Technologies, 474-476, Jul. 2006, Peer-reviwed
International conference proceedings, English

Online MDL-Markov analysis of a discussion Process in CSCL
Maomi Ueno; Toshio Okamoto
Proc. of Advanced Learning Technologies, 764-768, Jul. 2006
International conference proceedings, English

A management of e-Learning in Nagaoka University of Technology
Maomi Ueno; Mari Ueno; Minetaka Souma; Keita Kinoe; Hiroyuki Yamashita
Journal of Educational Technology, Japan Society for Educational Technology, 29, 3, 217-229, Jun. 2006, Peer-reviwed, Four years have passed since Nagaoka University of Technology started to provide e-Learning courses as formal courses. The university is providing 77 courses and the number of students of e-learning becomes more than 400 in 2004, and this is the domestic maximum scale. This paper proposes an original management method of decreasing teachers' load and administrators' load without lowering the learners' satisfaction even if the scale of the e-learning activity is expanded. The features of the management are as follows: 1. development of an e-learning practice model which enhances autonomous learning with knowledge creation, 2. utilization of a learning management system (LMS) with an intelligent agent which advices learners using data mining of learning log-data, 3. an utilization of an automatic on-line registration system, 4. an utilization of a contents authoring support system which supports development of e-learning contents, and 5. an utilization of a distance management system which enables several administrators to install the e-learning contents to the server from distance places. It is shown to be able to achieve the improvement of expansion of the scale of e-learning without lowering learner's satisfaction rating while decreasing the load of the administrators and the teachers, and shows the effectiveness of the proposed method.
Scientific journal, Japanese

Development of An Algorithm for Groupware Modeling for A Collaborative E-learning
Ikuo Kitagaki; Atsushi Hikita; Futao Fuang; Keiko Yokoyama; Keizo Nagaoka; Makoto Takeya; Nobuyoshi Yonezawa; Takako Akakura; Maomi Ueno; Yasuhiro Fujihara
PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON VIRTUAL LEARNING, 99-+, 2006, Peer-reviwed
International conference proceedings, English

On-line content analysis system using e-learning time data
Maomi Ueno; Keizo Nagaoka
2006 IEEE INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS AND APPLICATIONS, VOLS 1-3, 993-+, 2006, Peer-reviwed
International conference proceedings, English

Formal method of description supporting portfolio assessment
Yasuhiko Morimoto; Maomi Ueno; Isao Kikukawa; Setsuo Yokoyama; Youzou Miyadera
EDUCATIONAL TECHNOLOGY & SOCIETY, 9, 3, 88-99, 2006, Peer-reviwed
Scientific journal, English

A model for multiple‐choice problem selection
Maomi Ueno; Keizo Nagaoka
Electronics and Communications in Japan (Part III: Fundamental Electronic Science), 77, 2, 14-23, 1994, Peer-reviwed
Scientific journal, English

Preface: Special Issue on Advanced Methodologies for Bayesian Networks
Maomi Ueno
Jan. 2017, NEW GENERATION COMPUTING, 35, 1, 1-4, English, Others, 0288-3635, 1882-7055, WOS:000392321000001

CDO2-6 LiNGAMによるルーブリックの構成主義的学習に与える影響のモデル化(一般セッション 数学・統計(2))
山本 美紀; 植野 真臣
日本行動計量学会, 01 Sep. 2015, 日本行動計量学会大会発表論文抄録集, 43, 118-121, Japanese, 110010011395, AN10233727

ベイズ符号を用いた論文構成支援システムの開発と評価(一般セッション 教育・テスト)
宇都 雅輝; 植野 真臣
日本行動計量学会, 13 Sep. 2012, 日本行動計量学会大会発表論文抄録集, 40, 43-46, Japanese, 110009609148, AN10233727

Special Issue on Advanced Methodologies for Bayesian Networks Preface
Maomi Ueno
Jan. 2012, NEW GENERATION COMPUTING, 30, 1, 1-2, English, Others, 0288-3635, WOS:000300292600001

Toulmin Model based Argumentation Support System using Bayesian Network
UTO Masaki; SUZUKI Hiroaki; UENO Maomi
The purpose of this study is to develop a system which supports a persuasive argumentation. There are many related systems which focus on putting user's argumentation in the framework of the Toulmin model, which is known as a model for persuasive argumentation. However strengths of arguments are more important factors to prove the claim of an argumentation. Therefore this study develops an argumentation support system using a Bayesian network expression of the Toulmin model., The Institute of Electronics, Information and Communication Engineers, 08 Dec. 2011, IEICE technical report. Education technology, 111, 332, 41-46, Japanese, 0913-5685, 110009466761, AN10013163

Statistical larning for Bayesian networks
植野真臣
人工知能学会, Nov. 2010, Journal of the Japanese Society for Artificial Intelligence, 25, 6, 803-810, Japanese, Introduction other, 0912-8085, 110007880915, AN10067140

Analysis of Effect of Annotations Using Tablet PC in e-Learning
ANDO Masahiro; UENO Maomi
日本教育工学会, 03 Jul. 2010, Research report of JSET Conferences, 2010, 3, 199-206, Japanese, 10029780858, AN10420429

Article Structure Construction Support System based on Probabilistic Approach
UTO Masaki; MIYASAWA Yoshimitsu; SUZUKI Hiroaki; UENO Maomi
日本教育工学会, 03 Jul. 2010, Research report of JSET Conferences, 2010, 3, 11-18, Japanese, 10029780621, AN10420429

Mobile E-testing to Support Real Situation Assessment
MIYASAWA Yoshimitsu; UTO Masaki; ANDO Masahiro; UENO Maomi
日本教育工学会, 03 Jul. 2010, Research report of JSET Conferences, 2010, 3, 99-104, Japanese, 10029780730, AN10420429

Design of Learning Assessments
Atsushi Yoshikawa; Maomi Ueno
人工知能学会, Mar. 2010, Japanese Journal of Artificial Intelligence, 25, 2, 283-290, Japanese, Introduction other, 0912-8085, 110007580604, AN10067140

Sightseeing navigation by adaptive quiz using cellular phone
宮澤 芳光; 植野 真臣
教育システム情報学会, Jan. 2010, JSiSE research report, 24, 5, 46-51, Japanese, 1343-4527, 40016969389, AA11430186

Development of e-portfolio system for individualized educational support program using formal methods
永森正仁; 森本康彦; 植野真臣
Jan. 2010, JSiSE Research Report, Vol.24, No.5, pp.4-11

e-Testing:Advanced Testing Technology
Maomi Ueno
eテスティングとはコンピュータ上で実施されるテストの総称である.ただし,ペーパーテストを単にコンピュータに置き換えただけのものではない.具体的には,(1)マルチメディアによる質問項目の提示,(2)ネットワーク上でのテストの実施,(3)コンピュータの計算機能による自動作問,自動採点,自動テスト構成,自動データ解析,という三つの要素の有機的な要素で,テストの信頼性と妥当性の向上,セキュリティ向上,コスト削減,能力推定向上,テスト時間の短縮,など多くの利点を実現できるのである., The Institute of Electronics, Information and Communication Engineers, Dec. 2009, The journal of the institute of electronics, information and communication engineers, 92, 12, 1017-1021, Japanese, Introduction other, 0913-5693, 110007483239, AN1001339X

e-Testing:Advanced theories and technologies
Maomi Ueno
教育システム情報学会事務局, Aug. 2009, Journa lfor Information and Systems in Education, 26, 2, 204-217, Japanese, Peer-reviwed, Introduction other, 1341-4135, 40016793120, AN10474042

Development of a Description Language for Individualized Educational Support Program
永森正仁; 森本康彦; 植野真臣
本研究では，形式言語を用いて特別支援教育における「個別の教育支援計画」の構造を規定する記述文法を有する記述言語を開発した。, Jul. 2009, IEICE Technical Report, Vol.109, No.163, pp.19-24

Data Mining in e-Learning
Maomi Ueno
Dec. 2007, Japan Journal of Educational Technology, 31, 3, 271-283, Japanese, Peer-reviwed, Introduction other

Leading edge of educational technology researches:theories and applications of Learning Evaluation
Maomi Ueno
Jan. 2007, IEICE Inforamtion and Systems Soceity Journal, 11, 4, 6-7, Japanese, Introduction other, 10019914240

Analysis of directional characteristics of Educational technology researches
Minoru Nakayama; Maomi Ueno
教育工学における研究の評価指向として,独立な評価観点である「実践指向」と「理論指向」を抽出した.論文評価における評価指向の影響を検討するために,学会員と編集委員会委員との間で教育工学研究に対する評定値を比較したが,顕著な違いは見られなかった.また,実際に論文誌に掲載された「実践論文」と「一般論文」について,「実践指向」と「理論指向」で比較検討したところ,2つの論文カテゴリの間には有意な違いは見られなかった.さらに,調査した評定値に基づく論文の総合的評価をモデル化して分析したところ,論文には実践的貢献がある上に理論的貢献が求められていることを明らかにした.教育工学においては,実践指向を十分考慮して研究を進めたり,論文をまとめることの重要性を指摘した., Japan Society for Educational Technology, Sep. 2006, Journal of Educational Technology, 30, 1, 1-8, Japanese, Peer-reviwed, Introduction other, 1349-8290, 110006794596, AA11964147

Theories and Practice of advanced e-learning
Maomi Ueno
Jun. 2006, The Annual Report of Educational Psychology in Japan, 44, 126-137, Japanese, Peer-reviwed, Introduction other

Webアクセシビリティに関する評価項目の作成と適用(情報・認知)
植野 真臣; 安藤 雅洋
日本行動計量学会, Sep. 2004, 日本行動計量学会大会発表論文抄録集, 32, 328-331, Japanese, 110006373040, AN10233727

アイマークレコーダを用いたeラーニングのコンテンツ評価 (e-Learning向け動的デジタル教材の制作と配信)
安藤 雅洋; 植野 真臣
教育システム情報学会, Jul. 2004, 教育システム情報学会研究報告, 19, 2, 11-18, Japanese, 1343-4527, 40006381295, AA11430186

遠隔授業における Web レスポンスアナライザーの効果的利用法に関する研究
植野真臣
2003, 教育システム情報学会誌, 20, 1, 17-26, 80015796435, AN10474042

遠隔授業における Web レスポンスアナライザーの効果的利用に関する研究
植野真臣
2003, 教育システム情報学会誌, 20, 1, 1-10, 10029348811

Learning Log Database and Data Mining system for e-Learning -On-Line Statistical Outlier Detection of irregular learning processes
UENO Maomi
2002, Proc. of International Conference on Advanced Learning Technologies 2002, IEEE Computer Science, 436-438, 10012495740

確率ネットワークに基づく教育評価と授業設計
植野真臣
1994, 電子情報通信学会ワークショップ, 若手のための教育工学最前線, 10009972192

確率的グラフィカルモデル
Scholarly book, Japanese, Joint editor, 共立出版, 25 Jul. 2016

Advanced Methodologies for Bayesian networks
Joe Suzuki; Maomi Ueno
Scholarly book, English, Joint editor, Lecture Notes in Artificial Intelligence(LNAI9505) Springer, 01 Nov. 2015

Bayesian network
Maomi Ueno
Scholarly book, Japanese, Single work, 単著, コロナ社, 01 Jul. 2013, 9784339061031, ベイジアンネットワークの最先端について解説している

教育工学における学習評価
永岡慶三; 植野真臣; 山内裕平
Japanese, Editor, ミネルヴァ書房, Oct. 2012

Maomi Ueno, Takashi Isozaki
Proceeding of International Workshop on; Advanced Methodologies for; Bayesian networks
English, Supervisor, The Japanese Society for Artificial Intelligence, Nov. 2010

Handbook of Research on Methods and Techniques for Studying Virtual Communities: Paradigms and Phenomena
Maomi Ueno
English, Joint work, Intelligent LMS with an Agent that Learns from Log Data in a Virtual Community, Information Science Publishing, Sep. 2010

学習評価の新潮流
植野真臣; 荘島宏二朗
Japanese, Joint work, まえがき、第1章、第5章、第6章、第7章, 朝倉書店, Jun. 2010

E-learning Experiences and Future
Maomi Ueno
English, Joint work, Bayesian agent in e-Learning, In-Tech, Apr. 2010

Advances in learning processes
Masahiro Ando; Maomi Ueno
English, Joint work, An Analysis Using Eye-Mark Recorder of the Effectiveness of Presentation Methods for E-learning, In-Tech, Jan. 2010

Advances in learning processes
Pokpong Songmuang; Maomi Ueno
English, Joint work, E-Testing Construction Support System with some Prediction Tools, In-Tech, Jan. 2010

Advances in learning processes
Maomi Ueno
English, Joint work, An item response theory for peer assessment, In-Tech, Jan. 2010

e-Testing
Maomi Ueno; Keizo Nagaoka
Japanese, Editor, Feb. 2009

The Smart Study Guide
Maomi Ueno
Japanese, Single translation, Baihukan, 2009

e-Learning for Knowledge Society
Maomi Ueno
Japanese, Editor, 培風館, Jul. 2007

The 7th IEEE International Conference on Advanced Learning Technologies
Michael Spector; Demetrios Sampson; Toshio Okamoto, Kinshuk; Stefano A.Cerri; Maomi Ueno; Akihiro Kashihara
English, Supervisor, the IEEE Computer Society, Jul. 2007

Bayesian networks
Kazuo Shigemasu; Maomi Ueno; Yoichi Motomura
Japanese, Editor, Baihukan, Jun. 2006

整数計画法によるベイジアンネットワーク分類機の学習
植野真臣
Oral presentation, Japanese, 人工知能学会全国大会, Peer-reviewed
29 May 2024
28 May 2024- 31 May 2024

電気通信大学におけるIRTを用いた世界標準CBTの実践
植野真臣
Japanese, 日本テスト学会全国大会シンポジウム, Peer-reviewed
27 Aug. 2023

電気通信大学におけるCBTの取り組み
Maomi Ueno
Invited oral presentation, Japanese, 令和５年度全国大学入学者選抜研究連絡協議会大会（第18回）(大学入試センターセミナー) 「CBT(Computer Based Testing)における大学等機関の有機的な連携に向けて」, Invited
18 May 2023

Advanced technologies for e-Testing
Maomi Ueno
Invited oral presentation, English, International Conference on Computers in Education, The 18th International Conference on Computers in Education, ICCE 2010, APSCE(Asia-Pacific Society for Computers in Education ), Kualalumpul, Malaysia, International conference
Nov. 2010

離散数学
The University of Electro-Communications

大学院技術英語
The University of Electro-Communications

ベイズ的人工知能特論
The University of Electro-Communications

大学院技術英語
The University of Electro-Communications

大学院技術英語
電気通信大学

ベイズ的人工知能特論
The University of Electro-Communications

離散数学
The University of Electro-Communications

Advanced Theory on Bayesian Artificial Intelligence
The University of Electro-Communications

ベイズ的人工知能特論
電気通信大学

Discrete Mathematics
The University of Electro-Communications

離散数学
電気通信大学

Present
情報処理学会

Present
Japanese society for information and systems in education

Present
The Japan Association for Research on Testing

Present
日本教育工学会

Present
日本統計学会

Present
日本行動計量学会

Present
人工知能学会

電子情報通信学会

思考力評価を実現する人工知能を用いた適応型eテスティングの開発
植野真臣; 宇都雅輝; 宮澤芳光; 堤瑛美子
日本学術振興会, 科学研究費助成事業 基盤研究(A), 電気通信大学, 基盤研究(A), Principal investigator, 24H00739
Apr. 2024 - Mar. 2029

教育ビックデータの予測精度と解釈性を両立するBayesian Deep-IRT
植野 真臣; 宇都 雅輝
日本学術振興会, 科学研究費助成事業 挑戦的研究(萌芽), 電気通信大学, 挑戦的研究(萌芽), Principal investigator, 22K19825
Jun. 2022 - Mar. 2025

Development of e-Testing platform ensuring sustainable reliability
植野 真臣; 宇都 雅輝; 荒木 孝二; 鶴田 潤; 宮澤 芳光; 繁桝 算男; 大久保 智哉
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (S), The University of Electro-Communications, Grant-in-Aid for Scientific Research (S), Principal investigator, 本研究は，近年ニーズが高まっている筆記式試験や実技試験などのパフォーマンステストを含んで，高精度の測定誤差が持続するeテスティングプラットフォームを開発し，実運用によりその有効性を示すことを目指している．令和2年度は，提案プラットフォームを構成する以下の基礎技術について，令和元年度に引き続き研究を進めた．1）最大クリーク・アルゴリズムと整数計画法を用いて，テスト生成数をより向上させるアルゴリズムの開発，2）項目露出を一様とする等質テスト自動構成アルゴリズムの開発，3）項目露出を制御する等質適応型テストの開発，４）異質評価者の同定と継続的なトレーニング手法の開発，5）自然言語処理を用いた筆記試験における自動採点手法の開発．1）と2）については実装と評価実験が完了し，研究成果は電子情報通信学会に掲載された．3）についても順調に開発が進行しており，その成果は複数の国内学会で発表を行ない，Advances in Artificial Intelligenceに論文が掲載された．4）については，異質評価者の特性を表現できる新たな項目反応モデルを開発し，関連する成果が国際論文誌のBehaviormetrikaとBehavior Research Methods，および電子情報通信学会論文誌に掲載された．5）については，深層学習モデルと項目反応理論を組み込んだ新たな方法論を提案し，教育分野における人工知能活用に関する主要国際会議であるArtificial Intelligence in Educationに２件，自然言語処理分野の主要国際会議の一つであるInternational Conference on Computational Linguisticsに1件の論文が採択された． また，本研究テーマの主要課題の一つである「パフォーマンステストの継続運用を想定した運用デザインの設計とその実施支援システムの開発」に関しては，東京医科歯科大学での実証実験を想定して令和元年度に構築したシステムについて予備実験を進めた．, 19H05663
Jun. 2019 - Mar. 2024

Automated essay scoring using item response theory that considers rubric characteristics
Ueno Maomi
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Research (Exploratory), The University of Electro-Communications, Grant-in-Aid for Challenging Research (Exploratory), In automated essay scoring (AES), essays are automatically graded without human raters. Many AES models based on various manually designed features or various architectures of deep neural networks have been proposed over the past few decades. Each AES model has unique advantages and characteristics. Therefore, rather than using a single AES model, appropriate integration of predictions from various AES models is expected to achieve higher scoring accuracy. In the present paper, we develops 1) a new item response theory model that can estimate scores while considering characteristics of individual human-raters and rubric-items, and 2) a method that uses the item response theory model to integrate prediction scores from various AES models while taking into account differences in the characteristics of scoring behavior., 19K21751
Jun. 2019 - Mar. 2022

A large-scale e-Testing system and its applications
Maomi Ueno
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (A), The University of Electro-Communications, Grant-in-Aid for Scientific Research (A), This study developed a state-of-the-art e-Testing system, which realizes to construct the most number of uniform tests, and its operational guidelines based on actual practice of e-testing in several testing organizations. The results were published in the top international journals (IEEE transactions) and top conferences (AIED). The developed system and guidelines were evaluated in actual test organizations, such as National information technology engineer examination in INFORMATION-TECHNOLOGY PROMOTION AGENCY(IPA), Common achievement tests organization(CATE), Benesse corporation, Eiken, the synthetic personality inventory (SPI) examination, which are popular tests in Japan. We reported the details of each test organizations operation model of the e-testing system in the Japan Association for practice on testing (JART2017)., 15H01772
Apr. 2015 - Mar. 2020

Development of Adaptive Testing Based on Item Response Theory for Critical Thinking Test
Noboru WAKAYAMA,; KAJITANI Shinji
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C), Teikyo University, Grant-in-Aid for Scientific Research (C), Summary of research: Critical thinking is considered important. However, this area is wide and measuring the ability takes time. On the other hand, test theory measures ability efficiently using computer adaptive test (CAT) based on item response theory (IRT). Therefore, in this research, we aimed at the development of CAT based on IRT for the examination of the critical thinking test, and addressed the following issues. Research Results: Scale development: IRT promoted further scale development by optimal estimation of parameters. (1) Analytical thinking ability scale (2) Logic and reasoning ability scale (3) Reading and understanding ability scale．In addition, data were collected from the participants in the experiment to verify their reliability and validity., 15K01088
01 Apr. 2015 - 31 Mar. 2019

Probability-based adaptive hints to scaffold learning
Ueno Maomi
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research, The University of Electro-Communications, Grant-in-Aid for Challenging Exploratory Research, This study developed a scaffolding system that provides adaptive hints to adjust the predictive probability of the learner's successful performance to the previously determined certain value, using the item response theory (IRT). Furthermore, using the scaffolding system, we compared learning performances by changing the predictive probability. Results show that scaffolding to achieve 0.5 learner success probability provides the best performance. Additionally, results demonstrate that a scaffolding system providing 0.5 probability decreases the number of hints automatically as a fading function according to the learner's growth capability., 15K12407
Apr. 2015 - Mar. 2018

ePortfolio Which Facilitates Learning from Others
UENO Maomi; MORIMOTO Yasuhiko; HASHIMOTO Takamitsu; ANDO Mashiro; ISHII Tatatoshi; UTO Masaki; MIYASAWA Yoshimitsu
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), The University of Electro-Communications, Grant-in-Aid for Scientific Research (B), There are four characteristics that a learning community must have: (1) diversity of expertise among its members, (2) a shared objective of continually advancing the collective knowledge and skills, (3) an emphasis on learning how to learn, and (4) mechanisms for sharing what is learned. To enhance the development of learning communities, we developed an ePortfolio recommendation system. The unique features of this system are as follows: 1. The system recommends excellent other students who have similar learning histories with the user, 2. The system searches diverse others as much as possible. Namely, the system recommends excellent other students with similar learning histories to the target user but dissimilar each other. Actual trial use of the system demonstrates that the system does indeed promote learning from others，and supports sustainability of learning and deeper robust acquisition of knowledge; not superficial learning based on memorization., 24300281
Apr. 2012 - Mar. 2015

Uniform Test Form Assembly System in e-Testing
UENO Maomi; MORIMOTO Yasuhiko; HASHIMOTO Takamitsu; SONGMUANG Pokpong; ANDO Masahiro
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research, The University of Electro-Communications, Grant-in-Aid for Challenging Exploratory Research, Principal investigator, Educational assessments occasionally require uniform test forms for which each test form comprises a different set of items, but the forms meet equivalent test specifications (i.e., qualities indicated by test information functions based on item response theory). We propose two maximum clique algorithms for uniform test form assembly. The proposed methods can assemble uniform test forms with allowance of overlapping items among uniform test forms. First, we propose an exact method that maximizes the number of uniform test forms from an item pool. However, the exact method presents computational cost problems. To relax those problems, we propose an approximate method that maximizes the number of uniform test forms asymptotically. Accordingly, the proposed methods can use the item pool more efficiently than traditional methods can. We demonstrate the efficiency of the proposed methods using simulated and actual data., 24650549
Apr. 2012 - Mar. 2015

Development of multifunctional e-Portfolio system in e-learning
UENO Maomi; MORIMOTO Yasuhiko; FUJIWARA Yasuhiro; NAGAMORI Masahito; HASHIMOTO Takamitu; ANMA Fumihiko; ANDO Masahiro
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), The University of Electro-Communications, Grant-in-Aid for Scientific Research (B), Principal investigator, The e-Portfolio system(1) provides structured individual e-Portfolios and hyperlinks that enable students to quickly locate useful information created by others in various way,(2) features user-friendly, intelligent search capabilities making it easy to search for keywords and identify other bright students(based on past reports, test scores, peer assessment, and so on), and(3) supports an array of assessment techniques at all levels that not only encourage students to reflect upon their own learning but can be used to quickly identify the best students who consistently do excellent work. Actual trial use of the system demonstrates that the e-Portfolio system is effective., 21300305
2009 - 2011

Development of an Large-scale Type e-Testing System
UENO Maomi; MORIMOTO Yasuhiko; SYOUJIMA Koujiro; NAGAMORI Masahito; HASHIMOTO Takamitsu; ANDO Masahiro
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Exploratory Research, The University of Electro-Communications, Grant-in-Aid for Challenging Exploratory Research, Principal investigator, The purpose of this study is to develop a practical e-testing system which is consistently designed to unify various functions of the traditional computer based testing systems. The system is consists of Item Authoring System, Item Bank, Test Delivery System, e-Testing Construction Support System, Test Database, Data Analysis System, and Adaptive Testing System. The advantage features of the integrative system are 1. The test data stored in the server is automatically divided into each function and utilized for test analysis, item analysis, test construction, and adaptive testing, and 2. The system has various functions, therefore is used for various test purposes., 21650221
2009 - 2011

Intelligent LMS with an agent that learns from log data
UENO Maomi; OKAMOTO Tosio; ANMA Fumihiko; NAGAOKA Keizou; AKAKURA Takako; NAMATAME Yasuko; MORIMOTO Yasuhiko; FUJIWARA Yasuhiro; NAGAMORI Masahito; ANDO Masahiro
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), The University of Electro-Communications, Grant-in-Aid for Scientific Research (B), Principal investigator, 本研究では, 学習履歴ログ・データベースから, コンテンツに関する教材知識を習得して, 動機づけメッセージを自動生成するエージェントを搭載した知的LMSを開発した. 本システムの特徴は以下の通りである. 1)エージェントが決定木モデルにより学習者モデルを自動生成する. 2)エージェントが学習者モデルおよび学習者の現在の学習履歴データを用いて, 学習者の最終状態を予測する. そのためにデータベースに蓄積されるデータ量の増加に伴い,生成される学習者モデルはより正確になる. 3)エージェントは学習者の学習過程をデータベース中の優秀な成績を残した学習者の学習過程と比較, 分析し, 学習者に適応的なメッセージを生成する. システムを使用した講義と使用しなかった講義との比較で, システムの有効性が示された., 19300275
2007 - 2008

入試作問業務におけるナレッジ・マネージメント・システムの開発と実践的評価
植野 真臣; 永森 正仁; 安藤 雅洋; 吉村 宰; 荘島 宏二郎; 森本 康彦
日本学術振興会, 科学研究費助成事業 萌芽研究, 萌芽研究, Principal investigator, 本研究での実施の概要は、以下のとおりである。。 ・ナレッジ・マネジメント・システムの開発 イントラネット上で動作する入試業務のためのナレッジ・マネジメント・システムを開発している。シスァムの特徴は以下のとおりである。 1)入試センター試験のデータより、IRT(ltem Response Theory項目応答理論)や信頼性などの情報を自動的に計算し、提示する機能を持つ。 2)テスト作成に関する方法をシステム上のビデオ等で教え、テスト構成者が情報共有できる。 3)複数のテスト構成者が、インターネット上で協働してテスト構成を行うことを支援する協働テスト構成支援システムを開発した。最初にテストデータベースを用意し、それを用いてインターネット上でテス上構成を、一人、三人、五人と増やして行わせた。これらを複数のグループに行わせた結果、テストの妥当性(内容のミスのなさ)は、協働者の人数が増えるに従い、高くなることがわかったが、テストの信頼性(各項目の正誤とテスト得点との相関を項目の信頼性といい、その平均をテスト信頼性と呼ぶ)では、人数にはまったく関係なかったことがわかった。むしろ、人数が増えるとテスト構成に時間がかかりすぎ、信頼性が経る方向にあることがわかった。そこで、本システムでは、IRTを用いて構成中のテストの信頼性の予測値を計算し、次に選ぶべきテスト項目を推薦するというシステムを開発した。 開発したシステムを入試センターにおいて、いくつかの部会にサーバーを設置し、各試験委員に試用してもらい、アンケート、インタビュー等で評価を行った。作成プロセス・ログの整理とシステムの評価を行った。その結果、本システムの有効性が示された。, 17650257
2005 - 2006

Development of intellectual LMS that has the advanced data-mining function of the study history in e-Learning
UENO Maomi; NAGAMORI Masahito; ANDO Masahiro; MIKAMI Yoshiki; NAGAOKA Keizo; AKAKURA Takako
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Nagaoka University of Technology, Grant-in-Aid for Scientific Research (B), Principal investigator, This research reports the following : 1) The design of the structure of the learning histories data-base, 2) New data mining method for huge amount of the learning histories data for e-learning, and 3) a development of new LMS with a function of data mining for learning histories data. The details of the functions are as follows : 1) Online Detection of the learners with irregular learning processes 2) Online prediction function of future learner's state 3) Automatic construction of the learner model using the Bayesian belief network 4) Association Rule analysis of Learning histories data 5) On-line Gamma distribution analysis of learning time data 6) Online leaning agent using the data mining Actually, we used this system to actual learners. The results show that the system is very effective., 16300265
2004 - 2005

The Internet lesson system which has Web based computer testing mechanism
UENO Maomi; NAGAMORI Masahito; AKAKURA Takako; NAGAOKA Keizou
Japan Society for the Promotion of Science, Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (B), Nagaoka University of Technology, Grant-in-Aid for Scientific Research (B), Principal investigator, We developed a LMS with a web based computer testing mechanism. The features of the LMS are as follows : (1)Supporting test construction function based on test theory (2)Mounting of an item database based on test theory (3)The adapted type test function not only using item response theory but a new test theory (4)The detailed data analysis system using time required data, the change data of a reply, etc (5)Data-raining system for learning historical data is developed Contents distribution was performed as an e learning lesson using this LMS. 400 or more attendance students per year have been studying. I.II.was performed in theoretical research. I.Development of a new test theory II.Research and development of the data-mining technique, 14380076
2002 - 2003

インターネットを用いたオープンテスト・システム
植野 真臣
日本学術振興会, 科学研究費助成事業 奨励研究(A), 千葉大学, 奨励研究(A), Principal investigator, 申請者が提案した新しいテスト理論、ネットワーク型テスト理論を用いて、コンピュータテストシステムを構築する。システム構成は以下,の通りである。1. アイテム・バンクシステム、2. テスト実施システム、3. 情報量最大化原理に基づく適応型テスト機能である。4. 自動採点機能、5. インターネットによる遠隔受験機能である。開発言語はJavaによって開発され、受験者が遠隔授業などで学習した際に、その学力を測定することができる。また、適応型テスト機能により、少ない項目数で学習者がどこで、行き詰まったのかなどを知ることができる。実際にテスト(統計学)を教育学部学生に実施し、アンケート調査を行った結果、成績にはインターネットテストであることが影響しないが(よくも悪くもならないが)、混雑するときのコンピュータテストの動作が鈍いことの学習者へのストレスが指摘されていた。また、教師としては、使ってみたいが学生としてインターネットテストを受けることのメリットがもう一つ感じられないなどの指摘もあった。このような意味で、本コンピュータテストが、学習者の受験項目数を大幅に精度を落とさずに減少させていることを教示することによってこのイメージを大分好感のほうに転化できることであろうと考えられる。いずれにせよ、コンピュータテストを受けることの学習者のメリットをもう少し考えていかなければならない。現時点での問題は、アイテムバンク中の項目数が多くなり過ぎると、計算理論的に計算不可能となことがこのテスト理論の最も重要な問題点である。そのため、その解決法を提案した。これから、大規模型の診断型テストやCAIに応用できることと考えられる。, 09780147
1997 - 1998

確率ネットワークを組み込んだテスト理論の開発
植野 真臣
日本学術振興会, 科学研究費助成事業 奨励研究(A), 東京工業大学, 奨励研究(A), 著者は、これまで統計的意思決定理論の枠組より従来の局所独立性を仮定した項目応答理論(Item Response Theory,IRT)を発展させた新しいテスト理論、「ネットワーク型テスト理論」を提案している。 この研究の欠点は、教材構造を教師が主観的に決定していることにあった。ここでは、ネットワーク型テスト理論におけるネットワーク構造の構築手法を提案した。 具体的には、 (1)データを所与とした場合の教材構造の構築を教師の意思決定過程とみなし、アークの価値I(u,v)を定式化し、 (2)更に、アークの価値I(u,v)に基づいたネットワーク構成手法を提案した。 これらの実用的利点として、 ・アークの価値I(u,v)は非対称であり、順序性の制約を必要としない。 ・I(u,v)最大化原理に基づくネットワーク構成法によって、ネットワークの同型性(グラフ表現と確率的構造との同型性)が保証される。 ・この方法は逐次消去法による構造の探索手法に基づいており、探索空間の組合せ爆発が起こらず、規模の大きいネットワーク構築にも有効である。 ・構成されたネットワークは、教師の教授法についての有効なフィード・バックとなる。 が挙げられる。, 07780151
1995 - 1995

電通大CBT入試の報告
Appearance, 電気通信大学, 教科「情報」を含むアイテムバンク式CBT による大学入試の試み
07 Dec. 2024

世界標準を満たす電気通信大学のCBT入試
Appearance, 大学入試センター, Lecture
21 Aug. 2024

CBTは入試を変えられるか？～AI・世界最高峰技術で挑む国立大学入試プロジェクト～
Appearance, New Education Expo 2024, Seminar
06 Jun. 2024

NPO/NGO活動 UNESCO Japanコンサルタント
Organizing member, UNESCO Japanコンサルタント, civic_organization
01 Apr. 2003 - 31 Mar. 2006

NPO/NGO活動 UNESCO Bangkok ""ICT in Education Project""短期コンサルタント
Organizing member, UNESCO Bangkok ""ICT in Education Project""短期コンサルタント, civic_organization
01 Mar. 2005 - 31 Mar. 2005

NPO/NGO活動 国際協力事業団JICA短期専門家
Organizing member, 国際協力事業団JICA短期専門家, civic_organization
01 Mar. 2001 - 31 Mar. 2001

調布・電通大でコンピューター入試のシンポジウム 受験生や保護者も参加を
Other than myself, 調布新聞, Paper
Nov. 2024

ペーパーテストでは測れない思考力や創造力をみる―電気通信大学のCBT入試
学研, 進学情報, 特集 入学者選抜におけるCBTの可能性, Paper
Mar. 2024

パソコン入試は広がるか「情報」元年、普及へ出直し
日本経済新聞, Paper
12 Dec. 2023

The 18th International Conference on Education Data Mining, Senior Program Committee member
Academic society etc, Others, Jul. 2025 - Jul. 2025

26th International Conference on Artificial Intelligence in Education (AIED) Senior Program Committee member
Academic society etc, Others, Jul. 2025 - Jul. 2025

The 17th International Conference on Educational Data Mining
Academic society etc, Planning etc, Publishing chair, Senior Program Committee member, Dec. 2023 - Jul. 2024

25th International Conference on Artificial Intelligence in Education (AIED)
Academic society etc, Panel chair etc, Wide AIED track chair, Senior Program Committee member, Dec. 2023 - Jul. 2024

24th International Conference on Artificial Intelligence in Education (AIED2023), Local organizing chair, Seneor Program Committee member
Academic society etc, Planning etc, Local organizing chair, Senior Program Committee member, Apr. 2022 - 06 Jul. 2023

23rd International Conference on Artificial Intelligence in Education (AIED2022) Senior Program Committee member
Academic society etc, Others, Jul. 2022 - Jul. 2022

The Thirty-sixth AAAI Conference on Artificial Intelligence (AAAI-22) Program Committee member
Academic society etc, Others, 2022

他大学評価委員、各種研究費等の審査委員 文部科学省 統計エキスパート人材育成プロジェクト推進委員会委員
Review, 01 Apr. 2021

The Thirty-fifth AAAI Conference on Artificial Intelligence (AAAI-21) Program Committee member
Academic society etc, Others, 2021 - 2021

AAAI 2021 Workshop on AI Education(TIPCE2021) Program Committee member
Academic society etc, Others, 2021 - 2021

22th International Conference on Artificial Intelligence in Education (AIED2021) Program Committee member
Academic society etc, Others, 2021 - 2021

他大学評価委員、各種研究費等の審査委員 科学研究費委員会専門委員（統計科学）
Review, 01 Dec. 2019 - 30 Nov. 2020

他大学評価委員、各種研究費等の審査委員 大学入試センター CBT活用検討部会 委員
Review, Jun. 2020

他大学評価委員、各種研究費等の審査委員 大学入試センター CBTの活用に関する有識者会議 委員
Review, Jun. 2019 - May 2020

The Thirty-fourth AAAI Conference on Artificial Intelligence (AAAI-20) Program Committee member
Academic society etc, Others, 2020 - 2020

21st International Conference on Artificial Intelligence in Education (AIED2020) Program Committee member
Academic society etc, Others, 2020 - 2020

The Thirty-third AAAI Conference on Artificial Intelligence (AAAI-19) Program Committee member
Academic society etc, Others, 2019

20thInternational Conference on Artificial Intelligence in Education (AIED2019) Program Committee member
Academic society etc, Others, 2019

The Thirty-second AAAI Conference on Artificial Intelligence (AAAI-18)
Academic society etc, Others, 2018

19thInternational Conference on Artificial Intelligence in Education (AIED2018) Program Committee member
Academic society etc, Others, 2018

The Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) Program Committee member
Academic society etc, Others, 2017

18thInternational Conference on Artificial Intelligence in Education (AIED2017) Program Committee member
Academic society etc, Others, Program Committee member, 2017

The Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) Program Committee member
Academic society etc, Others, 2016

The Twenty-fifth International Conference on Artificial Intelligence (IJCAI-16) Program Committee member
Academic society etc, Others, 2016

他大学評価委員、各種研究費等の審査委員 文部科学省「情報教育の推進等に関する調査研究」委員
Review, 01 Nov. 2014 - 30 Dec. 2015

他大学評価委員、各種研究費等の審査委員 科学研究費委員会専門委員（統計科学）
Review, 01 Dec. 2014 - 30 Nov. 2015

他大学評価委員、各種研究費等の審査委員 科学研究費委員会専門委員（統計科学）
Review, 01 Dec. 2013 - 30 Nov. 2014

AISTATS (Seventh International Conference on Artificial Intelligence and Statistics), Program Committee member
Academic society etc, Others, 2014 - 2014

The Twenty-third International Conference on Artificial Intelligence and Statistics (IJCAI-13), Program Committee member
Academic society etc, Others, 2013 - 2013

他大学評価委員、各種研究費等の審査委員 IPA情報処理推進機構情報処理技術者試験eテスティング設計コンサルタント
Review, 01 Dec. 2010 - 30 Nov. 2012

Behaviormetrika (International Journal)
Supervision, Chief Editor, 01 Apr. 2012

他大学評価委員、各種研究費等の審査委員 国土交通省 観光庁「通訳案内士試験の実施方法検討委員会」委員
Review, 01 Apr. 2011 - 31 Mar. 2012

他大学評価委員、各種研究費等の審査委員 科学研究費委員会専門委員（教育工学）
Review, 01 Dec. 2010 - 30 Nov. 2011

他大学評価委員、各種研究費等の審査委員 IPA情報処理推進機構、情報処理者技術者試験委員
Review, 01 Apr. 2008 - 31 Mar. 2011

他大学評価委員、各種研究費等の審査委員 IPA情報処理推進機構CBT研究ワーキンググループ委員
Review, 01 Apr. 2008 - 31 Mar. 2011

他大学評価委員、各種研究費等の審査委員 科学研究費委員会専門委員（教育工学）
Review, 01 Dec. 2009 - 30 Nov. 2010

International workshop on Advanced Methodologies for Bayesian networks (AMBN2010 published by LNAI(Springer))
Supervision, Gneral Chair, Editor, 18 Oct. 2010 - 19 Oct. 2010

他大学評価委員、各種研究費等の審査委員 現代的教育ニーズ取組選定委員会委員（現代GPペーパーレフリー）
Review, 01 Apr. 2007 - 31 Mar. 2009

他大学評価委員、各種研究費等の審査委員 医療系大学共用試験実施評価機構専門委員
Review, 01 Apr. 2008

他大学評価委員、各種研究費等の審査委員 （社）情報処理学会情報規格調査会eテスティングJIS原案企画委員会委員
Review, 01 Apr. 2007 - 31 Mar. 2008

The 7th IEEE International Conference on Advanced Learning Technologies (ICALT2007)
Academic society etc, Planning etc, Conference Representative, Steering Chair, Editor, 18 Jul. 2007 - 20 Jul. 2007

他大学評価委員、各種研究費等の審査委員 文部科学省Japan Funds in Trust評価専門家
Review, 01 Apr. 2005 - 31 Mar. 2006

他大学評価委員、各種研究費等の審査委員 大学入試センター作問委員 数学②
Review, 01 Apr. 2004 - 31 Mar. 2006

他大学評価委員、各種研究費等の審査委員 文部科学省IT推進事業幹事校幹事
Review, 01 Apr. 2001 - 31 Mar. 2005

UNESCO-UNEVOC International Workshop, Human development for knowledge based society
Academic society etc, Others, General Chair, 17 Mar. 2005 - 18 Mar. 2005