Eda Okur

520 citations
19 papers · 183 · h-index 9

Impact in

Papers in

    • Intelligent Tutoring Systems and Adaptive Learning 8
    • Speech and dialogue systems 4
    • Topic Modeling 3
    • Natural Language Processing Techniques 2
    • Child Development and Digital Technology 3

Eda Okur

18 papers receiving 179 citations

Peers

Eda Okur
Comparison fields: 5 of 51
  • Computer Science Applications 72
  • Developmental and Educational Psychology 47
  • Human-Computer Interaction 16
  • Artificial Intelligence 73
  • Experimental and Cognitive Psychology 27
Replace Nicholas V. Mudrick with:
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Eda Okur relative to Nicholas V. Mudrick United States Nicholas V. Mudrick's profile →
Citations per field
00.5×4.9×
Nicholas V. Mudrick · 1×
Citations per year

Countries citing papers authored by Eda Okur

Since Specialization
Citations

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

Fields of papers citing papers by Eda Okur

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Eda Okur, 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 Eda Okur Line = papers co-authored together Eda Okur links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201960
2
Human Expert Labeling Process (HELP): Towards a Reliable Higher-order User State Labeling Process and Tool to Assess Student Engagement
201716
3 201715
4 201615
5 202211
6 202410
7 20189
8
Towards an Emotional Engagement Model: Can Affective States of a Learner be Automatically Detected in a 1: 1 Learning Scenario?
20169
9 20238
10 20237
11 20166
12 20225
13 20174
14 20232
15 20232
16 20202
17 20231
18 20201
19 20230

About Eda Okur

Eda Okur is a scholar working on Artificial Intelligence, Education, Computer Science Applications, Experimental and Cognitive Psychology and Information Systems, having authored 19 papers that have together received 183 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (8 papers), Online Learning and Analytics (5 papers), Speech and dialogue systems (4 papers), Topic Modeling (3 papers), Child Development and Digital Technology (3 papers), Emotion and Mood Recognition (3 papers), Natural Language Processing Techniques (2 papers) and Multimodal Machine Learning Applications (2 papers). The work is most often cited by research in Computer Science Applications (72 citations), Developmental and Educational Psychology (47 citations), Human-Computer Interaction (16 citations), Artificial Intelligence (73 citations) and Experimental and Cognitive Psychology (27 citations). Eda Okur has collaborated with scholars based in United States, Türkiye and Taiwan. Frequent co-authors include Neşe Alyüz, Sinem Aslan, Cagri Tanriover, Sidney K. D’Mello, Lama Nachman, Saurav Sahay, Bert Arnrich, Arzucan Özgür, Hakan Demir and Giuseppe Maria Raffa. Their work appears in journals such as Educational Technology Research and Development, Computer Speech & Language, British Journal of Educational Technology, Biological Trace Element Research and Computers and Education Artificial Intelligence.

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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