John Barr
Impact in
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- Teaching and Learning Programming
- Open Source Software Innovations
- Online Learning and Analytics
- E-Learning and Knowledge Management
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- Innovative Teaching and Learning Methods
- Educational Games and Gamification
Papers in
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- Teaching and Learning Programming 13
- Online Learning and Analytics 4
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- Innovative Teaching and Learning Methods 7
- Co-authors
- Tony Clear (8 shared papers)Michael Goldweber (8 shared papers)John Noll (4 shared papers)Sarah Beecham (4 shared papers)Mats Daniels (3 shared papers)Michael J. Oudshoorn (3 shared papers)Elizabeth Patitsas (3 shared papers)Renzo Davoli (2 shared papers)
- Journals
- IEEE Transactions on Human-Machine Systems (3 papers)Risk Analysis (2 papers)IEEE Software (2 papers)SIAM/ASA Journal on Uncertainty Quantification (1 paper)Journal for Research in Mathematics Education (1 paper)
- Partner nations
- United StatesCanadaNew Zealand
In The Last Decade
John Barr
36 papers receiving 301 citations
Peers
Comparison fields: 5 of 71
- Computer Science Applications 192
- Developmental and Educational Psychology 80
- Information Systems 128
- Communication 27
- Media Technology 28
Countries citing papers authored by John Barr
This map shows the geographic impact of John Barr'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 John Barr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Barr more than expected).
Fields of papers citing papers by John Barr
This network shows the impact of papers produced by John Barr. 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 John Barr. The network helps show where John Barr may publish in the future.
Co-authors
The 25 scholars most cited alongside John Barr, 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 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 49 | |
| 2 | 2015 | 49 | |
| 3 | 2017 | 37 | |
| 4 | 2012 | 24 | |
| 5 | 2017 | 22 | |
| 6 | 2012 | 21 | |
| 7 | 1994 | 13 | |
| 8 | 1997 | 11 | |
| 9 | 2010 | 11 | |
| 10 | 2022 | 10 | |
| 11 | 2023 | 6 | |
| 12 | 2021 | 6 | |
| 13 | 2021 | 5 | |
| 14 | 1994 | 5 | |
| 15 | 2011 | 5 | |
| 16 | 2013 | 5 | |
| 17 | 2019 | 5 | |
| 18 | 2015 | 4 | |
| 19 | 2012 | 3 | |
| 20 | 2020 | 3 |
About John Barr
John Barr is a scholar working on Computer Science Applications, Developmental and Educational Psychology, Information Systems, Education and Communication, having authored 42 papers that have together received 320 indexed citations. Recurring topics across this work include Teaching and Learning Programming (13 papers), Innovative Teaching and Learning Methods (7 papers), Software Engineering Techniques and Practices (5 papers), Experimental Learning in Engineering (4 papers), Online Learning and Analytics (4 papers), Knowledge Management and Sharing (3 papers), Probabilistic and Robust Engineering Design (3 papers) and Model-Driven Software Engineering Techniques (2 papers). The work is most often cited by research in Computer Science Applications (192 citations), Developmental and Educational Psychology (80 citations), Information Systems (128 citations), Communication (27 citations) and Media Technology (28 citations). John Barr has collaborated with scholars based in United States, Canada and New Zealand. Frequent co-authors include Tony Clear, Michael Goldweber, John Noll, Sarah Beecham, Mats Daniels, Michael J. Oudshoorn, Elizabeth Patitsas, Renzo Davoli, Samuel Mann and Herschel Rabitz. Their work appears in journals such as IEEE Transactions on Human-Machine Systems, Risk Analysis, IEEE Software, SIAM/ASA Journal on Uncertainty Quantification and Journal for Research in Mathematics Education.
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.