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Masaki Uto
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2020 – today
- 2024
- [c25]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 - [c24]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 - [c23]Yuto Tomikawa
, Masaki Uto
:
Difficulty-Controllable Multiple-Choice Question Generation for Reading Comprehension Using Item Response Theory. AIED Companion (1) 2024: 312-320 - [c22]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 - 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) - [c21]Misato Yamaura
, Itsuki Fukuda, Masaki Uto
:
Neural Automated Essay Scoring Considering Logical Structure. AIED 2023: 267-278 - [c20]Masaki Uto
, Yuto Tomikawa, Ayaka Suzuki:
Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory. BEA@ACL 2023: 119-129 - [c19]Masaki Uto
:
Neural Automated Short-Answer Grading Considering Examinee-Specific Features. ICALT 2023: 336-338 - [c18]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) - [c17]Takumi Shibata, Masaki Uto:
Analytic Automated Essay Scoring Based on Deep Neural Networks Integrating Multidimensional Item Response Theory. COLING 2022: 2917-2926 - [c16]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) - [c15]Itsuki Aomi, Emiko Tsutsumi, Masaki Uto
, Maomi Ueno:
Integration of Automated Essay Scoring Models Using Item Response Theory. AIED (2) 2021: 54-59 - [c14]Masaki Uto
:
A Multidimensional Item Response Theory Model for Rubric-Based Writing Assessment. AIED (1) 2021: 420-432 - [c13]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) - [c12]Masaki Uto
, Yuto Uchida:
Automated Short-Answer Grading Using Deep Neural Networks and Item Response Theory. AIED (2) 2020: 334-339 - [c11]Masaki Uto
, Masashi Okano:
Robust Neural Automated Essay Scoring Using Item Response Theory. AIED (1) 2020: 549-561 - [c10]Masaki Uto, Yikuan Xie, Maomi Ueno:
Neural Automated Essay Scoring Incorporating Handcrafted Features. COLING 2020: 6077-6088 - [c9]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
- [c8]Masaki Uto
:
Rater-Effect IRT Model Integrating Supervised LDA for Accurate Measurement of Essay Writing Ability. AIED (1) 2019: 494-506 - 2018
- [c7]Masaki Uto
, Maomi Ueno:
Item Response Theory Without Restriction of Equal Interval Scale for Rater's Score. AIED (2) 2018: 363-368 - [c6]Shouta Sugahara, Masaki Uto, Maomi Ueno:
Exact learning augmented naive Bayes classifier. PGM 2018: 439-450 - 2017
- [c5]Masaki Uto
, Nguyen Duc Thien, Maomi Ueno:
Group Optimization to Maximize Peer Assessment Accuracy Using Item Response Theory. AIED 2017: 393-405 - [c4]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
- [c3]Masaki Uto
, Maomi Ueno:
Item Response Model with Lower Order Parameters for Peer Assessment. AIED 2015: 800-803 - [c2]Masaki Uto
, Maomi Ueno:
Academic Writing Support System Using Bayesian Networks. ICALT 2015: 385-387 - [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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last updated on 2024-08-05 21:20 CEST by the dblp team
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