Stephen MacNeil

2.0k citations
63 papers · 1.1k indexed · 5 hit papers · h-index 15

Stephen MacNeil

55 papers receiving 1.0k citations

Hit Papers

The Widening Gap: The Benefits and Harms of...53202220262023202450100150

Peers

Stephen MacNeil
Comparison fields: 5 of 76
  • Health Informatics 143
  • Computer Science Applications 567
  • Software 87
  • Artificial Intelligence 402
  • Information Systems 246
Replace Brent N. Reeves with:
Brent N. Reeves United States
Irene Lee United States
Arto Hellas Finland
Juho Leinonen Finland
Benedict du Boulay United Kingdom
Chris Piech United States
Barbara Ericson United States
Shiyan Jiang United States
‪Marcos Román-González‬ Spain
Cary Laxer United States
Stephen MacNeil relative to Brent N. Reeves United States Brent N. Reeves's profile →
Citations per field
00.5×
Brent N. Reeves · 1×
Citations per year

Countries citing papers authored by Stephen MacNeil

Since Specialization
Citations

This map shows the geographic impact of Stephen MacNeil'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 Stephen MacNeil with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stephen MacNeil more than expected).

Fields of papers citing papers by Stephen MacNeil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stephen MacNeil. 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 Stephen MacNeil. The network helps show where Stephen MacNeil may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Stephen MacNeil, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Stephen MacNeil Line = papers co-authored together Stephen MacNeil links everyone, so they are left out of the graph.

All Works

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Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Bookbreakdown →
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About Stephen MacNeil

Stephen MacNeil is a scholar working on Computer Science Applications, Health Informatics and Software, having authored 63 papers that have together received 1.1k indexed citations. Recurring topics across this work include Online Learning and Analytics (20 papers), Teaching and Learning Programming (18 papers), Software Engineering Research (7 papers), Innovative Teaching and Learning Methods (7 papers), Experimental Learning in Engineering (5 papers), Data Visualization and Analytics (5 papers), Assistive Technology in Communication and Mobility (5 papers) and Software Testing and Debugging Techniques (5 papers). The work is most often cited by research in Health Informatics (143 citations), Computer Science Applications (567 citations) and Software (87 citations). Stephen MacNeil has collaborated with scholars based in United States, Finland and New Zealand. Frequent co-authors include Andrew Tran, Juho Leinonen, Paul Denny, Seth Bernstein, Arto Hellas, Sami Sarsa, Joanne Kim, James Prather, Brett A. Becker and Brent N. Reeves. Their work appears in journals such as Proceedings of the ACM on Human-Computer Interaction, Folia Phoniatrica et Logopaedica, Journal of Biomechanics, American Journal of Speech-Language Pathology and Computer Graphics Forum.

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.

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