Hao Cen
Impact in
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- Online Learning and Analytics
- Teaching and Learning Programming
- Artificial Intelligence top 5%
- Intelligent Tutoring Systems and Adaptive Learning
- AI-based Problem Solving and Planning
- Topic Modeling
- Machine Learning and Algorithms
Papers in
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- Intelligent Tutoring Systems and Adaptive Learning 6
- AI-based Problem Solving and Planning 3
- Cognitive Science and Mapping 1
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- Educational Technology and Assessment 3
- Co-authors
- Kenneth R. Koedinger (5 shared papers)Philip I. Pavlik (3 shared papers)Brian W. Junker (1 shared paper)Jack Mostow (1 shared paper)Joseph E. Beck (1 shared paper)Evandro Gouvêa (1 shared paper)
- Journals
- Educational Data Mining (2 papers)
- Partner nations
- United States
In The Last Decade
Hao Cen
6 papers receiving 369 citations
Peers
Comparison fields: 5 of 31
- Computer Science Applications 309
- Artificial Intelligence 375
- Developmental and Educational Psychology 99
- Computational Mathematics 2
- Information Systems 64
Countries citing papers authored by Hao Cen
This map shows the geographic impact of Hao Cen'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 Hao Cen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hao Cen more than expected).
Fields of papers citing papers by Hao Cen
This network shows the impact of papers produced by Hao Cen. 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 Hao Cen. The network helps show where Hao Cen may publish in the future.
Co-authors
The 6 scholars most cited alongside Hao Cen, 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 | Performance Factors Analysis --A New Alternative to Knowledge Tracing | 2009 | 270 |
| 2 | Is Over Practice Necessary? --Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining | 2007 | 60 |
| 3 | Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models | 2009 | 47 |
| 4 | Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor. | 2008 | 17 |
| 5 | Generalized learning factors analysis: improving cognitive models with machine learning | 2009 | 14 |
| 6 | Interactive Demonstration of a Generic Tool to Browse Tutor-Student Interactions | 2005 | 1 |
About Hao Cen
Hao Cen is a scholar working on Artificial Intelligence, Information Systems, Computer Science Applications, Developmental and Educational Psychology and Infectious Diseases, having authored 6 papers that have together received 409 indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (6 papers), AI-based Problem Solving and Planning (3 papers), Educational Technology and Assessment (3 papers), Online Learning and Analytics (2 papers), Innovative Teaching and Learning Methods (2 papers), Reading and Literacy Development (1 paper) and Cognitive Science and Mapping (1 paper). The work is most often cited by research in Computer Science Applications (309 citations), Artificial Intelligence (375 citations), Developmental and Educational Psychology (99 citations), Computational Mathematics (2 citations) and Information Systems (64 citations). Hao Cen has collaborated with scholars based in United States. Frequent co-authors include Kenneth R. Koedinger, Philip I. Pavlik, Brian W. Junker, Jack Mostow, Joseph E. Beck and Evandro Gouvêa. Their work appears in journals such as 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.