Dovan Rai
Impact in
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- Online Learning and Analytics
- Teaching and Learning Programming
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- Innovative Teaching and Learning Methods
- Educational Games and Gamification
Papers in
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- Intelligent Tutoring Systems and Adaptive Learning 7
- Machine Learning and Algorithms 1
- Bayesian Modeling and Causal Inference 1
- AI-based Problem Solving and Planning 1
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- Online Learning and Analytics 3
- Co-authors
- Kasia Müldner (3 shared papers)Ivon Arroyo (3 shared papers)Beverly Park Woolf (3 shared papers)Minghui Tai (2 shared papers)Joseph E. Beck (5 shared papers)Yue Gong (2 shared papers)Neil T. Heffernan (1 shared paper)Michael Wixon (1 shared paper)
- Journals
- International Journal of Artificial Intelligence in Education (2 papers)International Journal of Game-Based Learning (1 paper)Educational Data Mining (5 papers)
- Partner nations
- United StatesCanada
In The Last Decade
Dovan Rai
8 papers receiving 199 citations
Peers
Comparison fields: 5 of 41
- Computer Science Applications 131
- Developmental and Educational Psychology 93
- Artificial Intelligence 126
- Experimental and Cognitive Psychology 27
- Education 42
Countries citing papers authored by Dovan Rai
This map shows the geographic impact of Dovan Rai'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 Dovan Rai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dovan Rai more than expected).
Fields of papers citing papers by Dovan Rai
This network shows the impact of papers produced by Dovan Rai. 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 Dovan Rai. The network helps show where Dovan Rai may publish in the future.
Co-authors
The 9 scholars most cited alongside Dovan Rai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 147 | |
| 2 | Does Self-Discipline impact students' knowledge and learning? | 2009 | 21 |
| 3 | Using Dirichlet Priors to Improve Model Parameter Plausibility. | 2009 | 17 |
| 4 | The Opportunities and Limitations of Scaling Up Sensor-Free Affect Detection | 2014 | 15 |
| 5 | 2012 | 12 | |
| 6 | Exploring User Data From a Game-like Math Tutor: A Case Study in Causal Modeling. | 2011 | 7 |
| 7 | Analysis of a causal modeling approach: a case study with an educational intervention. | 2010 | 3 |
| 8 | 2018 | 1 |
About Dovan Rai
Dovan Rai is a scholar working on Artificial Intelligence, Computer Science Applications, Developmental and Educational Psychology, Experimental and Cognitive Psychology and Infectious Diseases, having authored 8 papers that have together received 223 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (7 papers), Online Learning and Analytics (3 papers), Innovative Teaching and Learning Methods (3 papers), Machine Learning and Algorithms (1 paper), Bayesian Modeling and Causal Inference (1 paper), Mental Health Research Topics (1 paper), Emotion and Mood Recognition (1 paper) and AI-based Problem Solving and Planning (1 paper). The work is most often cited by research in Computer Science Applications (131 citations), Developmental and Educational Psychology (93 citations), Artificial Intelligence (126 citations), Experimental and Cognitive Psychology (27 citations) and Education (42 citations). Dovan Rai has collaborated with scholars based in United States and Canada. Frequent co-authors include Kasia Müldner, Ivon Arroyo, Beverly Park Woolf, Minghui Tai, Joseph E. Beck, Yue Gong, Neil T. Heffernan, Michael Wixon and Winslow Burleson. Their work appears in journals such as International Journal of Artificial Intelligence in Education, International Journal of Game-Based Learning and Educational Data Mining.
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.