Kam Star

14 papers receiving 220 citations

Peers

Kam Star
Comparison fields: 5 of 52
  • Experimental and Cognitive Psychology 126
  • Signal Processing 40
  • Computer Science Applications 19
  • Computer Vision and Pattern Recognition 63
  • Human-Computer Interaction 16
Replace Mariusz Szwoch with:
Mariusz Szwoch Poland
Hung‐Hsuan Huang Japan
Dairazalia Sánchez-Cortés Switzerland
Wataru Tsukahara Japan
Svetlana Stoyanchev United States
Loredana Cerrato Ireland
Swadha Gupta India
Su‐Youn Yoon United States
Brandon Roy United States
Atiwong Suchato Thailand
Kam Star relative to Mariusz Szwoch Poland Mariusz Szwoch's profile →
Citations per field
00.5×3.2×
Mariusz Szwoch · 1×
Citations per year

Countries citing papers authored by Kam Star

Since Specialization
Citations

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

Fields of papers citing papers by Kam Star

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

14 of 14 papers shown
#Work
1 2019160
2 201717
3 201315
4 201814
5 20234
6 20114
7 20163
8
Towards Social Media Platform Integration with an Applied Gaming Ecosystem
20153
9
Creating opportunities to learn social skills at school using digital games
20163
10 20152
11 20141
12
D3.5 – First Storytelling Framework
20161
13
D2.1 2nd User requirements for gamification of prosocial learning
20161
14
ProsocialLearn: D2.5 evaluation strategy and protocols
20151

About Kam Star

Kam Star is a scholar working on Developmental and Educational Psychology, Sociology and Political Science, Artificial Intelligence, Education and Management Information Systems, having authored 14 papers that have together received 229 indexed citations. Recurring topics across this work include Educational Games and Gamification (9 papers), Artificial Intelligence in Games (3 papers), Digital Games and Media (3 papers), Intelligent Tutoring Systems and Adaptive Learning (2 papers), Higher Education and Employability (2 papers), Color perception and design (1 paper), Cloud Computing and Resource Management (1 paper) and Innovative Education and Learning Practices (1 paper). The work is most often cited by research in Experimental and Cognitive Psychology (126 citations), Signal Processing (40 citations), Computer Science Applications (19 citations), Computer Vision and Pattern Recognition (63 citations) and Human-Computer Interaction (16 citations). Kam Star has collaborated with scholars based in United Kingdom, Greece and Belgium. Frequent co-authors include Jie Shen, Elnar Hajiyev, Robert Walecki, Vedhas Pandit, Maximilian Schmitt, Fabien Ringeval, Antoine Toisoul, Jean Kossaifi, Jing Han and Yannis Panagakis. Their work appears in journals such as IEEE Computational Intelligence Magazine, IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Game-Based Learning, Information and Pure (Coventry University).

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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