Avi Segal

30 papers receiving 320 citations

Peers

Avi Segal
Comparison fields: 5 of 65
  • Computer Science Applications 143
  • Ecological Modeling 39
  • Developmental and Educational Psychology 74
  • Health Informatics 7
  • Artificial Intelligence 116
Replace Gi Woong Choi with:
Gi Woong Choi United States
Natercia Valle United States
Ha‐Kyung Kong United States
Jun Oshima Japan
Danhua Zhou China
James Aczel United Kingdom
Dorottya Demszky United States
Xian Peng China
Julia Cambre United States
Zilong Pan United States
Avi Segal relative to Gi Woong Choi United States Gi Woong Choi's profile →
Citations per field
00.5×9.8×
Gi Woong Choi · 1×
Citations per year

Countries citing papers authored by Avi Segal

Since Specialization
Citations

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

Fields of papers citing papers by Avi Segal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Avi Segal, 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 Avi Segal Line = papers co-authored together Avi Segal links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201837
2 201634
3
EduRank: A Collaborative Filtering Approach to Personalization in E-learning.
201431
4 202131
5 201928
6 201525
7 202214
8 202113
9
Intervention strategies for increasing engagement in crowdsourcing: platform, predictions, and experiments
201612
10 202011
11 20249
12 20209
13 20219
14 20188
15 20187
16 20186
17 20236
18 20216
19 20225
20 20234

About Avi Segal

Avi Segal is a scholar working on Computer Science Applications, Artificial Intelligence, Information Systems, Ecological Modeling and Developmental and Educational Psychology, having authored 31 papers that have together received 329 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (9 papers), Online Learning and Analytics (8 papers), Mobile Crowdsensing and Crowdsourcing (7 papers), Species Distribution and Climate Change (6 papers), Educational Games and Gamification (4 papers), Scientific Computing and Data Management (4 papers), Expert finding and Q&A systems (3 papers) and Innovative Teaching and Learning Methods (3 papers). The work is most often cited by research in Computer Science Applications (143 citations), Ecological Modeling (39 citations), Developmental and Educational Psychology (74 citations), Health Informatics (7 citations) and Artificial Intelligence (116 citations). Avi Segal has collaborated with scholars based in Israel, United Kingdom and United States. Frequent co-authors include Kobi Gal, Guy Shani, Bracha Shapira, Osama Swidan, Baruch B. Schwarz, Marina Jirotka, Grant Miller, Eliane Sommerfeld, R. J. Simpson and Kevin Page. Their work appears in journals such as IEEE Transactions on Learning Technologies, Journal of the Society for Social Work and Research, Educational Technology Research and Development, Frontiers in Medicine and Journal of Psychiatric Research.

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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