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2020 – today
- 2024
[j7]Yuto Tomikawa
, Ayaka Suzuki, Masaki Uto:
Adaptive Question-Answer Generation With Difficulty Control Using Item Response Theory and Pretrained Transformer Models. IEEE Trans. Learn. Technol. 17: 2240-2252 (2024)- [c33]Kota Aramaki, Masaki Uto:
Collaborative Essay Evaluation with Human and Neural Graders Using Item Response Theory Under a Nonequivalent Groups Design. AIED Companion (2) 2024: 79-87 - [c32]Naoki Shindo, Masaki Uto:
ChatGPT-Based Virtual Standardized Patient that Amends Overly Detailed Responses in Objective Structured Clinical Examinations. AIED Companion (1) 2024: 263-269 - [c31]Yuto Tomikawa, Masaki Uto:
Difficulty-Controllable Multiple-Choice Question Generation for Reading Comprehension Using Item Response Theory. AIED Companion (1) 2024: 312-320 - [c30]Masaki Uto, Yuto Takahashi:
Neural Automated Essay Scoring for Improved Confidence Estimation and Score Prediction Through Integrated Classification and Regression. AIED Companion (1) 2024: 444-451 - [c29]Takumi Shibata, Masaki Uto:
Enhancing Cross-prompt Automated Essay Scoring by Selecting Training Data Based on Reinforcement Learning. EcalLAC@AIED 2024 - [c28]Masaki Uto, Yuto Tomikawa, Ayaka Suzuki:
Question Difficulty Prediction Based on Virtual Test-Takers and Item Response Theory. EcalLAC@AIED 2024 - [c27]Teruyoshi Goto, Yuto Tomikawa, Masaki Uto:
Enhancing Diversity in Difficulty-Controllable Question Generation for Reading Comprehension via Extended T5. ICCE 2024 - [c26]Yuto Tomikawa, Masaki Uto:
Difficulty-Controllable Reading Comprehension Question Generation Considering the Difficulty of Reading Passages. ICCE 2024 - [c25]Minoru Nakayama, Masaki Uto, Satoru Kikuchi, Hiroh Yamamoto:
Predicting Factor Scores of Critical Thinking Ability From Features of Essay Texts. ITHET 2024: 1-6 - [c24]Minoru Nakayama, Masaki Uto, Marco Temperini, Filippo Sciarrone:
Appropriate Number of Raters for IRT Based Peer Assessment Evaluation of Programming Skills. ITHET 2024: 1-5 - [c23]Minoru Nakayama, Masaki Uto, Satoru Kikuchi, Hiroh Yamamoto:
Estimating Scores of Critical Thinking Ability Using Essay Text Assessments. IV 2024: 1-6 - 2023
- [j6]Masaki Uto, Itsuki Aomi, Emiko Tsutsumi, Maomi Ueno:
Integration of Prediction Scores From Various Automated Essay Scoring Models Using Item Response Theory. IEEE Trans. Learn. Technol. 16(6): 983-1000 (2023) - [c22]Misato Yamaura, Itsuki Fukuda, Masaki Uto:
Neural Automated Essay Scoring Considering Logical Structure. AIED 2023: 267-278 - [c21]Masaki Uto, Yuto Tomikawa, Ayaka Suzuki:
Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory. BEA@ACL 2023: 119-129 - [c20]Masaki Uto:
Neural Automated Short-Answer Grading Considering Examinee-Specific Features. ICALT 2023: 336-338 - [c19]Minoru Nakayama, Masaki Uto, Satoru Kikuchi, Hiroh Yamamoto:
Feasibility of Prediction of Student's Characteristics Using Texts of Essays Written During a Fully Online Course. IV 2023: 204-209 - 2022
- [j5]Minoru Nakayama, Filippo Sciarrone, Marco Temperini, Masaki Uto:
An Item Response Theory Approach to Enhance Peer Assessment Effectiveness in Massive Open Online Courses. Int. J. Distance Educ. Technol. 20(1): 1-19 (2022) - [c18]Takumi Shibata, Masaki Uto:
Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory. COLING 2022: 2917-2926 - [c17]Minoru Nakayama, Filippo Sciarrone, Marco Temperini, Masaki Uto:
Evaluation of Programming Skills via Peer Assessment and IRT Estimation Techniques. ITHET 2022: 1-8 - 2021
- [j4]Masaki Uto, Masashi Okano:
Learning Automated Essay Scoring Models Using Item-Response-Theory-Based Scores to Decrease Effects of Rater Biases. IEEE Trans. Learn. Technol. 14(6): 763-776 (2021) - [c16]Itsuki Aomi, Emiko Tsutsumi, Masaki Uto, Maomi Ueno:
Integration of Automated Essay Scoring Models Using Item Response Theory. AIED (2) 2021: 54-59 - [c15]Masaki Uto:
A Multidimensional Item Response Theory Model for Rubric-Based Writing Assessment. AIED (1) 2021: 420-432 - [c14]Minoru Nakayama, Masaki Uto, Marco Temperini, Filippo Sciarrone:
Estimating Ability of Programming Skills using IRT based Peer Assessments. ITHET 2021: 1-6 - 2020
- [j3]Masaki Uto, Yoshimitsu Miyazawa, Yoshihiro Kato, Koji Nakajima, Hajime Kuwata:
Time- and Learner-Dependent Hidden Markov Model for Writing Process Analysis Using Keystroke Log Data. Int. J. Artif. Intell. Educ. 30(2): 271-298 (2020) - [j2]Masaki Uto, Nguyen Duc Thien, Maomi Ueno:
Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory and Integer Programming. IEEE Trans. Learn. Technol. 13(1): 91-106 (2020) - [c13]Masaki Uto, Yuto Uchida:
Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory. AIED (2) 2020: 334-339 - [c12]Masaki Uto, Masashi Okano:
Robust Neural Automated Essay Scoring Using Item Response Theory. AIED (1) 2020: 549-561 - [c11]Masaki Uto, Yikuan Xie, Maomi Ueno:
Neural Automated Essay Scoring Incorporating Handcrafted Features. COLING 2020: 6077-6088 - [c10]Minoru Nakayama, Filippo Sciarrone, Masaki Uto, Marco Temperini:
Impact of the number of peers on a mutual assessment as learner's performance in a simulated MOOC environment using the IRT model. IV 2020: 486-490
2010 – 2019
- 2019
- [c9]Masaki Uto:
Rater-Effect IRT Model Integrating Supervised LDA for Accurate Measurement of Essay Writing Ability. AIED (1) 2019: 494-506 - 2018
- [c8]Masaki Uto, Maomi Ueno:
Item Response Theory Without Restriction of Equal Interval Scale for Rater's Score. AIED (2) 2018: 363-368 - [c7]Shouta Sugahara, Masaki Uto, Maomi Ueno:
Exact learning augmented naive Bayes classifier. PGM 2018: 439-450 - 2017
- [c6]Masaki Uto, Nguyen Duc Thien, Maomi Ueno:
Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory. AIED 2017: 393-405 - [c5]Kazuki Natori, Masaki Uto, Maomi Ueno:
Consistent Learning Bayesian Networks with Thousands of Variables. AMBN 2017: 57-68 - 2016
- [j1]Masaki Uto, Maomi Ueno:
Item Response Theory for Peer Assessment. IEEE Trans. Learn. Technol. 9(2): 157-170 (2016) - 2015
- [c4]Masaki Uto, Maomi Ueno:
Item Response Model with Lower Order Parameters for Peer Assessment. AIED 2015: 800-803 - [c3]Masaki Uto, Maomi Ueno:
Academic Writing Support System Using Bayesian Networks. ICALT 2015: 385-387 - [c2]Nguyen Duc Thien, Masaki Uto, Yu Abe, Maomi Ueno:
Reliable Peer Assessment for Team-project-based Learning using Item Response Theory. ICCE 2015 - [c1]Kazuki Natori, Masaki Uto, Yu Nishiyama, Shuichi Kawano, Maomi Ueno:
Constraint-Based Learning Bayesian Networks Using Bayes Factor. AMBN@JSAI-isAI 2015: 15-31
Coauthor Index
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