Avi Segal

579 total citations
30 papers, 320 citations indexed

About

Avi Segal is a scholar working on Computer Science Applications, Artificial Intelligence and Information Systems. According to data from OpenAlex, Avi Segal has authored 30 papers receiving a total of 320 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Science Applications, 12 papers in Artificial Intelligence and 6 papers in Information Systems. Recurrent topics in Avi Segal's work include Intelligent Tutoring Systems and Adaptive Learning (9 papers), Online Learning and Analytics (8 papers) and Mobile Crowdsensing and Crowdsourcing (7 papers). Avi Segal is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (9 papers), Online Learning and Analytics (8 papers) and Mobile Crowdsensing and Crowdsourcing (7 papers). Avi Segal collaborates with scholars based in Israel, United Kingdom and United States. Avi Segal's co-authors include Kobi Gal, Guy Shani, Bracha Shapira, Osama Swidan, Baruch B. Schwarz, Gil Zalsman, Avi Valevski, Eric Horvitz, Corinne Levy and Eliane Sommerfeld and has published in prestigious journals such as Scientific Reports, Journal of Psychiatric Research and International Journal of Human-Computer Studies.

In The Last Decade

Avi Segal

29 papers receiving 311 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Avi Segal Israel	10	143	118	74	46	40	30	320
Gi Woong Choi United States	11	193 1.3×	50 0.4×	77 1.0×	97 2.1×	4 0.1×	24	371
Joseph A. Harsh United States	11	23 0.2×	27 0.2×	48 0.6×	19 0.4×	10 0.3×	20	368
Dorottya Demszky United States	8	51 0.4×	153 1.3×	51 0.7×	15 0.3×	2 0.1×	22	333
William Man-Yin Cheung Hong Kong	6	225 1.6×	107 0.9×	54 0.7×	81 1.8×		6	395
Tatsunori Matsui Japan	7	113 0.8×	113 1.0×	48 0.6×	69 1.5×	1 0.0×	42	340
M. Taboada Spain	9	140 1.0×	168 1.4×	48 0.6×	63 1.4×		27	446
Woonhee Sung United States	8	162 1.1×	26 0.2×	154 2.1×	50 1.1×	2 0.1×	16	298
Clare Baek United States	10	178 1.2×	69 0.6×	52 0.7×	47 1.0×		16	338
Sven Charleer Belgium	11	199 1.4×	55 0.5×	114 1.5×	54 1.2×		25	352
Natercia Valle United States	8	154 1.1×	26 0.2×	185 2.5×	53 1.2×	3 0.1×	16	351

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-authorship network of co-authors of Avi Segal

This figure shows the co-authorship network connecting the top 25 collaborators of Avi Segal. A scholar is included among the top collaborators of Avi Segal 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 Avi Segal. Avi Segal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Levi‐Belz, Yossi, et al.. (2025). Predicting imminent suicide risk in a crisis hotline chat using machine learning. Scientific Reports. 15(1). 44742–44742.

Benatov, Joy, et al.. (2024). Predicting suicide risk in real‐time crisis hotline chats integrating machine learning with psychological factors: Exploring the black box. Suicide and Life-Threatening Behavior. 54(3). 416–424. 8 indexed citations

Segal, Avi, et al.. (2023). Combining Psychological Theory with Language Models for Suicide Risk Detection. 2430–2438. 4 indexed citations

Segal, Avi, et al.. (2023). Transparency-Check: An Instrument for the Study and Design of Transparency in AI-based Personalization Systems. 1(1). 1–18. 6 indexed citations

Rafner, Janet, Miroslav Gajdacz, Arthur Hjorth, et al.. (2022). Mapping Citizen Science through the Lens of Human-Centered AI. 9(1). 66–95. 12 indexed citations

Segal, Avi, et al.. (2022). Addressing Popularity Bias in Citizen Science. 17–23. 3 indexed citations

Zalsman, Gil, Corinne Levy, Eliane Sommerfeld, et al.. (2021). Suicide-related calls to a national crisis chat hotline service during the COVID-19 pandemic and lockdown. Journal of Psychiatric Research. 139. 193–196. 30 indexed citations

Gal, Kobi, et al.. (2021). New Methods for Confusion Detection in Course Forums: Student, Teacher, and Machine. IEEE Transactions on Learning Technologies. 14(5). 665–679. 5 indexed citations

Facciotti, Marc T., et al.. (2021). Seeding Course Forums using the Teacher-in-the-Loop. 22–31. 1 indexed citations

10.

Fulginiti, Anthony, Avi Segal, Jennifer Wilson, et al.. (2021). Getting to the Root of the Problem: A Decision-Tree Analysis for Suicide Risk Among Young People Experiencing Homelessness. Journal of the Society for Social Work and Research. 13(2). 327–352. 4 indexed citations

11.

Cohen, Anat, et al.. (2021). Personalizing mathematical content in educational applets repository: human teacher versus machine-based considerations. Educational Technology Research and Development. 69(3). 1505–1528. 1 indexed citations

12.

Gal, Kobi, et al.. (2020). Intelligent recommendations for citizen science. Conference on Recommender Systems. 2697. 3 indexed citations

13.

Gal, Kobi, Avi Segal, Amy X. Zhang, et al.. (2020). #Confused and beyond. 589–594. 11 indexed citations

14.

Hershkovitz, Arnon, et al.. (2019). Teacher vs. Algorithm: Double-blind experiment of content sequencing in mathematics.. Educational Data Mining. 2 indexed citations

15.

Segal, Avi, Kobi Gal, Guy Shani, & Bracha Shapira. (2019). A difficulty ranking approach to personalization in E-learning. International Journal of Human-Computer Studies. 130. 261–272. 27 indexed citations

16.

Leeuwen, Anouschka van, Nikol Rummel, Vincent Aleven, et al.. (2018). Symposium orchestration tools for teachers in the context of individual and collaborative learning: What information do teachers need and what do they do with it?. International Conference of Learning Sciences. 2. 1227–1234. 6 indexed citations

17.

Segal, Avi, et al.. (2018). Combining Difficulty Ranking with Multi-Armed Bandits to Sequence Educational Content. arXiv (Cornell University). 7 indexed citations

18.

Segal, Avi, et al.. (2016). Intervention strategies for increasing engagement in crowdsourcing: platform, predictions, and experiments. International Joint Conference on Artificial Intelligence. 3861–3867. 12 indexed citations

19.

Segal, Avi, Kobi Gal, R. J. Simpson, et al.. (2015). Improving Productivity in Citizen Science through Controlled Intervention. 331–337. 25 indexed citations

20.

Segal, Avi, et al.. (2014). EduRank: A Collaborative Filtering Approach to Personalization in E-learning.. Educational Data Mining. 68–75. 31 indexed citations

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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