| Neural automated essay scoring incorporating handcrafted features M Uto, Y Xie, M Ueno Proceedings of the 28th international conference on computational …, 2020 | 143 | 2020 |
| A review of deep-neural automated essay scoring models M Uto Behaviormetrika 48 (2), 459-484, 2021 | 96 | 2021 |
| Item Response Theory for Peer Assessment M Uto, M Ueno IEEE Transactions on Learning Technologies, 2015 | 90 | 2015 |
| Empirical comparison of item response theory models with rater's parameters M Uto, M Ueno Heliyon 4 (5), 2018 | 64 | 2018 |
| Robust neural automated essay scoring using item response theory M Uto, M Okano Artificial Intelligence in Education: 21st International Conference, AIED …, 2020 | 55 | 2020 |
| A generalized many-facet Rasch model and its Bayesian estimation using Hamiltonian Monte Carlo M Uto, M Ueno Behaviormetrika 47 (2), 469-496, 2020 | 53 | 2020 |
| Learning Automated Essay Scoring Models Using Item Response Theory-Based Scores to Decrease Effects of Rater Biases M Uto, M Okano IEEE Transactions on Learning Technologies, 2022 | 41 | 2022 |
| Automated short-answer grading using deep neural networks and item response theory M Uto, Y Uchida Artificial Intelligence in Education: 21st International Conference, AIED …, 2020 | 40 | 2020 |
| Social constructivist approach of motivation: social media messages recommendation system S Louvigné, M Uto, Y Kato, T Ishii Behaviormetrika 45, 133-155, 2018 | 32 | 2018 |
| Accuracy of performance-test linking based on a many-facet Rasch model M Uto Behavior Research Methods 53 (4), 1440-1454, 2021 | 31 | 2021 |
| Group optimization to maximize peer assessment accuracy using item response theory and integer programming M Uto, DT Nguyen, M Ueno IEEE Transactions on Learning Technologies 13 (1), 91-106, 2019 | 28 | 2019 |
| Rater-effect IRT model integrating supervised LDA for accurate measurement of essay writing ability M Uto Artificial Intelligence in Education: 20th International Conference, AIED …, 2019 | 28 | 2019 |
| A multidimensional generalized many-facet Rasch model for rubric-based performance assessment M Uto Behaviormetrika 48 (2), 425-457, 2021 | 27 | 2021 |
| Difficulty-controllable neural question generation for reading comprehension using item response theory M Uto, Y Tomikawa, A Suzuki Proceedings of the 18th Workshop on Innovative Use of NLP for Building …, 2023 | 26 | 2023 |
| Constraint-based learning Bayesian networks using Bayes factor K Natori, M Uto, Y Nishiyama, S Kawano, M Ueno Advanced Methodologies for Bayesian Networks: Second International Workshop …, 2015 | 26 | 2015 |
| Item response theory without restriction of equal interval scale for rater’s score M Uto, M Ueno Artificial Intelligence in Education: 19th International Conference, AIED …, 2018 | 25 | 2018 |
| Consistent learning Bayesian networks with thousands of variables K Natori, M Uto, M Ueno Advanced Methodologies for Bayesian Networks, 57-68, 2017 | 25 | 2017 |
| Integration of prediction scores from various automated essay scoring models using item response theory M Uto, I Aomi, E Tsutsumi, M Ueno IEEE Transactions on Learning Technologies 16 (6), 983-1000, 2023 | 24 | 2023 |
| A Bayesian many-facet Rasch model with Markov modeling for rater severity drift M Uto Behavior research methods 55 (7), 3910-3928, 2023 | 20 | 2023 |
| Diverse reports recommendation system based on latent Dirichlet allocation M Uto, S Louvigné, Y Kato, T Ishii, Y Miyazawa Behaviormetrika 44, 425-444, 2017 | 19 | 2017 |
Maomi UenoThe university of Electro-communicationsVerified email at ai.is.uec.ac.jp
