Tanya Nazaretsky
- Computer Science Applications top 2%
- Artificial Intelligence top 10%
- Education top 10%
- Information Systems top 10%
- Health Informatics top 2%
- Co-authors
- Giora AlexandronMutlu CukurovaMoriah ArielyTanja KäserJamie N. MikeskaBeata Beigman KlebanovThiemo WambsganßAntoine Bosselut
- Topics
- Explainable Artificial Intelligence (XAI) (4 papers)Innovative Teaching and Learning Methods (3 papers)Online Learning and Analytics (3 papers)
- Journals
- Journal of Research in Science TeachingBritish Journal of Educational TechnologyJournal of Computer Assisted Learning
- Partner nations
- IsraelSwitzerlandUnited Kingdom
In The Last Decade
Tanya Nazaretsky
11 papers receiving 373 citations
Hit Papers
Peers
Comparison fields: 5 of 62
- Computer Science Applications 189
- Artificial Intelligence 139
- Education 92
- Information Systems 74
- Health Informatics 60
Countries citing papers authored by Tanya Nazaretsky
This map shows the geographic impact of Tanya Nazaretsky'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 Tanya Nazaretsky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tanya Nazaretsky more than expected).
Fields of papers citing papers by Tanya Nazaretsky
This network shows the impact of papers produced by Tanya Nazaretsky. 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 Tanya Nazaretsky. The network helps show where Tanya Nazaretsky may publish in the future.
Co-authorship network of co-authors of Tanya Nazaretsky
This figure shows the co-authorship network connecting the top 25 collaborators of Tanya Nazaretsky. A scholar is included among the top collaborators of Tanya Nazaretsky based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Tanya Nazaretsky. Tanya Nazaretsky is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | The critical role of trust in adopting AI-powered educational technology for learning: An instrument for measuring student perceptionsbreakdown → | 17 |
| 2 | 0 | |
| 3 | 4 | |
| 4 | 10 | |
| 5 | 3 | |
| 6 | 9 | |
| 7 | 26 | |
| 8 | Teachers' trust in | 216 |
| 9 | 27 | |
| 10 | 70 | |
| 11 | 1 | |
| 12 | Kappa Learning: A New Item-Similarity Method for Clustering Educational Items from Response Data. | 6 |
About Tanya Nazaretsky
Tanya Nazaretsky is a scholar working on Computer Science Applications, Artificial Intelligence and Developmental and Educational Psychology, having authored 12 papers that have together received 389 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (4 papers), Innovative Teaching and Learning Methods (3 papers) and Online Learning and Analytics (3 papers). The work is most often cited by research in Health Informatics (60 citations), Computer Science Applications (189 citations) and Safety Research (43 citations). Tanya Nazaretsky has collaborated with scholars based in Israel, Switzerland and United Kingdom. Frequent co-authors include Giora Alexandron, Mutlu Cukurova, Moriah Ariely, Tanja Käser, Jamie N. Mikeska, Beata Beigman Klebanov, Thiemo Wambsganß and Antoine Bosselut. Their work appears in journals such as Journal of Research in Science Teaching, British Journal of Educational Technology and Journal of Computer Assisted 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.