Ramkumar Rajendran

444 citations
31 papers · 268 · h-index 9

Impact in

Papers in

Ramkumar Rajendran

25 papers receiving 255 citations

Peers

Ramkumar Rajendran
Comparison fields: 5 of 48
  • Computer Science Applications 127
  • Developmental and Educational Psychology 113
  • Human-Computer Interaction 22
  • Artificial Intelligence 102
  • Experimental and Cognitive Psychology 32
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Wookhee Min United States
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Akihiro Kashihara Japan
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Amali Weerasinghe Australia
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Citations per field
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Citations per year

Countries citing papers authored by Ramkumar Rajendran

Since Specialization
Citations

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

Fields of papers citing papers by Ramkumar Rajendran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Ramkumar Rajendran, 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 Ramkumar Rajendran Line = papers co-authored together Ramkumar Rajendran 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 201970
2 201844
3 201826
4 201316
5 202016
6 201815
7 201915
8 201812
9 20239
10
Predicting Learning by Analyzing Eye-Gaze Data of Reading Behavior.
20188
11
Studying the Interactions between Components of Self Regulated Learning in Open Ended Learning Environments.
20184
12 20254
13 20244
14 20134
15 20243
16 20223
17 20232
18 20192
19 20232
20 20192

About Ramkumar Rajendran

Ramkumar Rajendran is a scholar working on Computer Science Applications, Artificial Intelligence, Developmental and Educational Psychology, Human-Computer Interaction and Control and Systems Engineering, having authored 31 papers that have together received 268 indexed citations. Recurring topics across this work include Online Learning and Analytics (14 papers), Innovative Teaching and Learning Methods (11 papers), Intelligent Tutoring Systems and Adaptive Learning (9 papers), Virtual Reality Applications and Impacts (4 papers), Teaching and Learning Programming (4 papers), Visual and Cognitive Learning Processes (3 papers), Gaze Tracking and Assistive Technology (2 papers) and Smart Grid Security and Resilience (2 papers). The work is most often cited by research in Computer Science Applications (127 citations), Developmental and Educational Psychology (113 citations), Human-Computer Interaction (22 citations), Artificial Intelligence (102 citations) and Experimental and Cognitive Psychology (32 citations). Ramkumar Rajendran has collaborated with scholars based in India, United States and Australia. Frequent co-authors include Gautam Biswas, Sahana Murthy, Sridhar Iyer, Roger Azevedo, Elizabeth B. Cloude, Michelle Taub, Jaclyn Ocumpaugh, Luc Paquette, Ryan S. Baker and Judithe Sheard. Their work appears in journals such as Smart Learning Environments, IEEE Transactions on Learning Technologies, Computers and Education Artificial Intelligence, IEEE Transactions on Visualization and Computer Graphics and Learning and Instruction.

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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