Wookhee Min

48 papers receiving 422 citations

Peers

Wookhee Min
Comparison fields: 5 of 65
  • Artificial Intelligence 244
  • Developmental and Educational Psychology 176
  • Computer Science Applications 163
  • Computer Vision and Pattern Recognition 65
  • Sociology and Political Science 54
Replace Tanja Käser with:
Tanja Käser Switzerland
Katerina Mangaroska Norway
Alina von Davier United States
Kay Berkling Germany
Myroslava O. Dzikovska United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Wookhee Min

Since Specialization
Citations

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

Fields of papers citing papers by Wookhee Min

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Wookhee Min

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

All Works

20 of 20 papers shown
#WorkIndexed citations
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Enhancing Student Competency Models for Game-Based Learning with a Hybrid Stealth Assessment Framework.
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Automated Assessment of Computer Science Competencies from Student Programs with Gaussian Process Regression.
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Predicting Early and Often: Predictive Student Modeling for Block-Based Programming Environments.
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Improving Stealth Assessment in Game-Based Learning with LSTM-Based Analytics
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Deep LSTM-Based Goal Recognition Models for Open-World Digital Games.
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Player goal recognition in open-world digital games with long short-term memory networks
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Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data.
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FLARE: An Open Source Toolkit for Creating Expressive User Interfaces for Serious Games
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About Wookhee Min

Wookhee Min is a scholar working on Computer Science Applications, Developmental and Educational Psychology and Artificial Intelligence, having authored 52 papers that have together received 444 indexed citations. Recurring topics across this work include Educational Games and Gamification (27 papers), Intelligent Tutoring Systems and Adaptive Learning (17 papers) and Teaching and Learning Programming (16 papers). The work is most often cited by research in Computer Science Applications (163 citations), Developmental and Educational Psychology (176 citations) and Artificial Intelligence (244 citations). Wookhee Min has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Bradford Mott, James C. Lester, Jonathan Rowe, Eric Wiebe, Kristy Elizabeth Boyer, Andrew Emerson, Kyungjin Park, Andy Smith, Pengcheng Wang and Eun Young Ha. Their work appears in journals such as British Journal of Educational Technology, Journal of Research on Technology in Education and IEEE Transactions on Learning Technologies.

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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