Masaki Uto
Impact in
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- Online Learning and Analytics
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Intelligent Tutoring Systems and Adaptive Learning
- Text Readability and Simplification
Papers in
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- Topic Modeling 11
- Natural Language Processing Techniques 5
- Intelligent Tutoring Systems and Adaptive Learning 4
- Advanced Text Analysis Techniques 4
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- Psychometric Methodologies and Testing 10
- Co-authors
- Maomi Ueno (11 shared papers)Yoshihiro Kato (3 shared papers)Y. Tomikawa (4 shared papers)Koji Nakajima (1 shared paper)Ayaka Suzuki (2 shared papers)Marco Temperini (4 shared papers)Filippo Sciarrone (4 shared papers)Minoru Nakayama (6 shared papers)
In The Last Decade
Masaki Uto
28 papers receiving 317 citations
Peers
Comparison fields: 5 of 52
- Computer Science Applications 60
- Artificial Intelligence 191
- Management Science and Operations Research 55
- Health Informatics 5
- Statistics and Probability 30
Countries citing papers authored by Masaki Uto
This map shows the geographic impact of Masaki Uto's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Masaki Uto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Masaki Uto more than expected).
Fields of papers citing papers by Masaki Uto
This network shows the impact of papers produced by Masaki Uto. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Masaki Uto. The network helps show where Masaki Uto may publish in the future.
Co-authors
The 9 scholars most cited alongside Masaki Uto, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 59 | |
| 2 | 2021 | 49 | |
| 3 | 2015 | 34 | |
| 4 | 2020 | 23 | |
| 5 | 2018 | 23 | |
| 6 | 2021 | 18 | |
| 7 | 2019 | 14 | |
| 8 | 2023 | 14 | |
| 9 | 2020 | 13 | |
| 10 | 2017 | 13 | |
| 11 | 2021 | 10 | |
| 12 | 2017 | 9 | |
| 13 | 2022 | 9 | |
| 14 | 2021 | 8 | |
| 15 | Consistent Learning Bayesian Networks with Thousands of Variables | 2017 | 6 |
| 16 | 2020 | 4 | |
| 17 | 2015 | 3 | |
| 18 | 2024 | 2 | |
| 19 | 2024 | 2 | |
| 20 | 2023 | 2 |
About Masaki Uto
Masaki Uto is a scholar working on Artificial Intelligence, Management Science and Operations Research, Information Systems, Education and Statistics and Probability, having authored 31 papers that have together received 326 indexed citations. Recurring topics across this work include Topic Modeling (11 papers), Psychometric Methodologies and Testing (10 papers), Natural Language Processing Techniques (5 papers), Student Assessment and Feedback (5 papers), Intelligent Tutoring Systems and Adaptive Learning (4 papers), Advanced Text Analysis Techniques (4 papers), Online Learning and Analytics (4 papers) and Software Engineering Research (3 papers). The work is most often cited by research in Computer Science Applications (60 citations), Artificial Intelligence (191 citations), Management Science and Operations Research (55 citations), Health Informatics (5 citations) and Statistics and Probability (30 citations). Masaki Uto has collaborated with scholars based in Japan and Italy. Frequent co-authors include Maomi Ueno, Yoshihiro Kato, Y. Tomikawa, Koji Nakajima, Ayaka Suzuki, Marco Temperini, Filippo Sciarrone, Minoru Nakayama and Hiroh Yamamoto. Their work appears in journals such as IEEE Transactions on Learning Technologies, Behavior Research Methods, International Journal of Artificial Intelligence in Education, International Journal of Distance Education Technologies and PLoS ONE.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.