James E. Willis

25 papers receiving 287 citations

Peers

James E. Willis
Comparison fields: 5 of 94
  • Computer Science Applications 88
  • Biochemistry 93
  • Health Informatics 13
  • Clinical Biochemistry 35
  • Cancer Research 31
Replace Debra C. Henly with:
Debra C. Henly Australia
Judith B. Howard United States
Michal Růžička Czechia
Daniell DiFrancesca United States
Shu-Chu Chen Taiwan
Hui-Chun Yang Taiwan
Cédric d’Ham France
James B. Sumner United States
Paul Laidler United Kingdom
Marvin C. Pankaskie United States
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Citations per year

Countries citing papers authored by James E. Willis

Since Specialization
Citations

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

Fields of papers citing papers by James E. Willis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James E. Willis. 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 James E. Willis. The network helps show where James E. Willis may publish in the future.

Co-authors

The 24 scholars most cited alongside James E. Willis, 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 James E. Willis Line = papers co-authored together James E. Willis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 196273
2 201662
3 196447
4
Ethics, Big Data, and Analytics: A Model for Application
201324
5 196319
6 201714
7 19628
8 19897
9 19827
10 20176
11 19876
12 20195
13 19835
14 19665
15
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
20145
16 19895
17 20204
18 19883
19
Ethical Discourse: Guiding the Future of Learning Analytics
20143
20 20153

About James E. Willis

James E. Willis is a scholar working on Computer Science Applications, Molecular Biology, Radiation, Artificial Intelligence and Biochemistry, having authored 30 papers that have together received 321 indexed citations. Recurring topics across this work include Online Learning and Analytics (8 papers), Amino Acid Enzymes and Metabolism (3 papers), Educational Games and Gamification (3 papers), Electron and X-Ray Spectroscopy Techniques (3 papers), Enzyme function and inhibition (3 papers), X-ray Spectroscopy and Fluorescence Analysis (3 papers), Polyamine Metabolism and Applications (3 papers) and Intelligent Tutoring Systems and Adaptive Learning (2 papers). The work is most often cited by research in Computer Science Applications (88 citations), Biochemistry (93 citations), Health Informatics (13 citations), Clinical Biochemistry (35 citations) and Cancer Research (31 citations). James E. Willis has collaborated with scholars based in United States, Russia and South Africa. Frequent co-authors include H.J. Sallach, Paul Prinsloo, Sharon Slade, Matthew D. Pistilli, John P. Campbell, Victor M. H. Borden, Daniel T. Hickey, Arlene Chung, Michael D. Falkoff and Creed W. Abell. Their work appears in journals such as Clinical Chemistry, Educational Technology Research and Development, Archives of Biochemistry and Biophysics, Sixteenth Century Journal and International Journal of Public Administration.

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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