John Stamper
- Computer Science Applications top 0.2%
- Online Learning and Analytics 37
- Teaching and Learning Programming 11
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- Innovative Teaching and Learning Methods 14
- Artificial Intelligence top 2%
- Intelligent Tutoring Systems and Adaptive Learning 38
- AI-based Problem Solving and Planning 12
- Machine Learning and Algorithms 4
- Topic Modeling 4
- Software top 10%
- Information Systems top 5%
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- Online and Blended Learning 4
- Co-authors
- Manolis MavrikisKenneth R. KoedingerTiffany BarnesMichael EagleElizabeth A. McLaughlinSatabdi BasuShuchi GroverMarie Bienkowski
- Cited by
- Computer Science ApplicationsDevelopmental and Educational PsychologyArtificial Intelligence
- Journals
- International Journal of Artificial Intelligence in Education (3 papers)Journal of Learning Analytics (2 papers)Home Health Care Services Quarterly (1 paper)
- Partner nations
- United StatesNew ZealandSwitzerland
In The Last Decade
John Stamper
64 papers receiving 1.1k citations
Hit Papers
Peers
Comparison fields: 5 of 86
- Computer Science Applications 778
- Developmental and Educational Psychology 431
- Artificial Intelligence 665
- Software 40
- Information Systems 187
Countries citing papers authored by John Stamper
This map shows the geographic impact of John Stamper'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 John Stamper with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Stamper more than expected).
Fields of papers citing papers by John Stamper
This network shows the impact of papers produced by John Stamper. 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 John Stamper. The network helps show where John Stamper may publish in the future.
Co-authorship network
The 25 scholars most cited alongside John Stamper, 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 | Using Knowledge Component Modeling to Increase Domain Understanding in a Digital Learning Game. | 2019 | 6 |
| 2 | Early Detection of Wheel Spinning: Comparison across Tutors, Models, Features, and Operationalizations. | 2019 | 1 |
| 3 | Linkage Objects for Generalized Instruction in Coding (LOGIC). | 2018 | 2 |
| 4 | Predictive Student Modeling for Interventions in Online Classes. | 2018 | 3 |
| 5 | Predicting Individualized Learner Models across Tutor Lessons. | 2018 | 2 |
| 6 | Peer Tutor Matching for Introductory Programming: Data-Driven Methods to Enable New Opportunities for Help. | 2018 | 2 |
| 7 | Automatic Peer Tutor Matching: Data-Driven Methods to Enable New Opportunities for Help. | 2017 | 2 |
| 8 | Beyond Log Files: Using Multi-Modal Data Streams towards Data-Driven KC Model Improvement. | 2016 | 5 |
| 9 | Extracting Measures of Active Learning and Student Self-Regulated Learning Strategies from MOOC Data. | 2016 | 2 |
| 10 | Proceedings of the Seventh International Conference on Educational Data Mining (EDM) (7th, London, United Kingdom, July 4-7, 2014). | 2014 | 1 |
| 11 | Proceedings of the 7th International Conference on Educational Data Mining.breakdown → | 2014 | 452 |
| 12 | Online Education: A Unique Opportunity for Cognitive Scientists to Integrate Research and Practice | 2013 | 7 |
| 13 | A Comparison of Model Selection Metrics in DataShop. | 2013 | 7 |
| 14 | An Algorithm for Reducing the Complexity of Interaction Networks. | 2013 | 4 |
| 15 | The rise of the super experiment | 2012 | 13 |
| 16 | EDM and the 4th Paradigm of Scientific Discovery - Reflections on KDD Cup 2010. | 2011 | 1 |
| 17 | Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing. | 2011 | 29 |
| 18 | Proceedings of the International Conference on Educational Data Mining (EDM) (4th, Eindhoven, the Netherlands, July 6-8, 2011). | 2011 | 2 |
| 19 | A Data Driven Approach to the Discovery of Better Cognitive Models. | 2010 | 6 |
| 20 | An unsupervised, frequency-based metric for selecting hints in an MDP-based tutor. | 2009 | 3 |
About John Stamper
John Stamper is a scholar working on Computer Science Applications, Developmental and Educational Psychology, Artificial Intelligence, Software and Information Systems, having authored 66 papers that have together received 1.2k indexed citations. Recurring topics across this work include Intelligent Tutoring Systems and Adaptive Learning (38 papers), Online Learning and Analytics (37 papers), Innovative Teaching and Learning Methods (14 papers), AI-based Problem Solving and Planning (12 papers), Teaching and Learning Programming (11 papers), Machine Learning and Algorithms (4 papers), Topic Modeling (4 papers) and Online and Blended Learning (4 papers). The work is most often cited by research in Computer Science Applications (778 citations), Developmental and Educational Psychology (431 citations), Artificial Intelligence (665 citations), Software (40 citations) and Information Systems (187 citations). John Stamper has collaborated with scholars based in United States, New Zealand and Switzerland. Frequent co-authors include Manolis Mavrikis, Kenneth R. Koedinger, Tiffany Barnes, Michael Eagle, Elizabeth A. McLaughlin, Satabdi Basu, Shuchi Grover, Marie Bienkowski, Ryan S. Baker and Emma Brunskill. Their work appears in journals such as International Journal of Artificial Intelligence in Education, Journal of Learning Analytics, Home Health Care Services Quarterly, Journal of Travel Medicine and ACM Transactions on Computing Education.
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.