Matthew Nyaaba
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
- Computer Science Applications top 10%
- Online Learning and Analytics
Papers in
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- Online Learning and Analytics 7
-
- Explainable Artificial Intelligence (XAI) 5
- Co-authors
- Xiaoming Zhaı (6 shared papers)Wenchao Ma (2 shared papers)Gengchen Mai (1 shared paper)Zhengliang Liu (1 shared paper)Tianming Liu (1 shared paper)Lehong Shi (2 shared papers)Arne Bewersdorff (2 shared papers)Ehsan Latif (1 shared paper)
- Journals
- International Journal of Science Education (1 paper)IEEE Transactions on Learning Technologies (1 paper)Science & Education (1 paper)Education Sciences (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United StatesGhanaSingapore
In The Last Decade
Matthew Nyaaba
16 papers receiving 152 citations
Peers
Comparison fields: 5 of 41
- Health Informatics 29
- Computer Science Applications 49
- Safety Research 19
- Developmental and Educational Psychology 22
- Artificial Intelligence 45
Countries citing papers authored by Matthew Nyaaba
This map shows the geographic impact of Matthew Nyaaba'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 Matthew Nyaaba with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthew Nyaaba more than expected).
Fields of papers citing papers by Matthew Nyaaba
This network shows the impact of papers produced by Matthew Nyaaba. 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 Matthew Nyaaba. The network helps show where Matthew Nyaaba may publish in the future.
Co-authors
The 13 scholars most cited alongside Matthew Nyaaba, 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 | 2024 | 41 | |
| 2 | 2024 | 32 | |
| 3 | 2025 | 19 | |
| 4 | 2024 | 13 | |
| 5 | 2024 | 9 | |
| 6 | 2024 | 8 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 6 | |
| 9 | 2025 | 5 | |
| 10 | 2023 | 5 | |
| 11 | 2023 | 5 | |
| 12 | 2024 | 3 | |
| 13 | 2022 | 3 | |
| 14 | 2021 | 2 | |
| 15 | 2024 | 1 | |
| 16 | 2026 | 1 | |
| 17 | 2025 | 0 | |
| 18 | 2026 | 0 |
About Matthew Nyaaba
Matthew Nyaaba is a scholar working on Computer Science Applications, Artificial Intelligence, Health Informatics, Safety Research and Education, having authored 18 papers that have together received 159 indexed citations. Recurring topics across this work include Online Learning and Analytics (7 papers), Explainable Artificial Intelligence (XAI) (5 papers), Artificial Intelligence in Healthcare and Education (3 papers), Child Development and Education (2 papers), Ethics and Social Impacts of AI (2 papers), Educational Research and Pedagogy (1 paper), Family and Disability Support Research (1 paper) and Educational Strategies and Epistemologies (1 paper). The work is most often cited by research in Health Informatics (29 citations), Computer Science Applications (49 citations), Safety Research (19 citations), Developmental and Educational Psychology (22 citations) and Artificial Intelligence (45 citations). Matthew Nyaaba has collaborated with scholars based in United States, Ghana and Singapore. Frequent co-authors include Xiaoming Zhaı, Wenchao Ma, Gengchen Mai, Zhengliang Liu, Tianming Liu, Lehong Shi, Arne Bewersdorff, Ehsan Latif, Kok‐Sing Tang and Grant Cooper. Their work appears in journals such as International Journal of Science Education, IEEE Transactions on Learning Technologies, Science & Education, Education Sciences and DOAJ (DOAJ: Directory of Open Access Journals).
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.