Maya Usher
Impact in
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- Online Learning and Analytics
- E-Learning and Knowledge Management
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
Papers in
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- Online Learning and Analytics 6
- E-Learning and Knowledge Management 3
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- Online and Blended Learning 2
- Student Assessment and Feedback 2
- Co-authors
- Miri Barak (8 shared papers)Arnon Hershkovitz (4 shared papers)Alona Forkosh‐Baruch (1 shared paper)Hossam Haick (1 shared paper)Meital Amzalag (2 shared papers)Eytan Ruppin (1 shared paper)Marc Jansen (1 shared paper)Orly Fuhrman (1 shared paper)
In The Last Decade
Maya Usher
19 papers receiving 354 citations
Maya Usher's Hit Papers
Peers
Comparison fields: 5 of 66
- Computer Science Applications 108
- Health Informatics 23
- Education 171
- Developmental and Educational Psychology 58
- Information Systems and Management 26
Countries citing papers authored by Maya Usher
This map shows the geographic impact of Maya Usher'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 Maya Usher with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maya Usher more than expected).
Fields of papers citing papers by Maya Usher
This network shows the impact of papers produced by Maya Usher. 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 Maya Usher. The network helps show where Maya Usher may publish in the future.
Co-authors
The 11 scholars most cited alongside Maya Usher, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2017 | 92 | |
| 2 | 2019 | 59 | |
| 3 | 2021 | 45 | |
| 4 | 2024 | 43 | |
| 5 | 2019 | 29 | |
| 6 | Generative AI vs. instructor vs. peer assessments: a comparison of grading and feedback in higher education Hit paper breakdown → | 2025 | 17 |
| 7 | 2021 | 17 | |
| 8 | 2022 | 14 | |
| 9 | 2021 | 13 | |
| 10 | 2023 | 10 | |
| 11 | 2020 | 10 | |
| 12 | 2025 | 7 | |
| 13 | 2025 | 4 | |
| 14 | 2025 | 3 | |
| 15 | 2023 | 3 | |
| 16 | 1990 | 3 | |
| 17 | 2024 | 2 | |
| 18 | 2025 | 1 | |
| 19 | 2024 | 1 | |
| 20 | 2025 | 1 |
About Maya Usher
Maya Usher is a scholar working on Computer Science Applications, Education, Artificial Intelligence, Biomedical Engineering and Information Systems, having authored 20 papers that have together received 374 indexed citations. Recurring topics across this work include Online Learning and Analytics (6 papers), Ethics and Social Impacts of AI (3 papers), Artificial Intelligence in Healthcare and Education (3 papers), Biomedical and Engineering Education (3 papers), E-Learning and Knowledge Management (3 papers), Online and Blended Learning (2 papers), Design Education and Practice (2 papers) and Student Assessment and Feedback (2 papers). The work is most often cited by research in Computer Science Applications (108 citations), Health Informatics (23 citations), Education (171 citations), Developmental and Educational Psychology (58 citations) and Information Systems and Management (26 citations). Maya Usher has collaborated with scholars based in Israel, Germany and Sweden. Frequent co-authors include Miri Barak, Arnon Hershkovitz, Alona Forkosh‐Baruch, Hossam Haick, Meital Amzalag, Eytan Ruppin, Marc Jansen, Orly Fuhrman, Ofra Amir and Ido Roll. Their work appears in journals such as Assessment & Evaluation in Higher Education, International Journal of Artificial Intelligence in Education, International Journal of STEM Education, Computers & Education and Online Learning.
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.