Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
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).
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 of co-authors of John Stamper
This figure shows the co-authorship network connecting the top 25 collaborators of John Stamper.
A scholar is included among the top collaborators of John Stamper 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 John Stamper. John Stamper 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
1.
Xing, Wanli, Scott A. Crossley, Paul Denny, et al.. (2025). The Use of Large Language Models in Education. International Journal of Artificial Intelligence in Education. 35(2). 439–443.4 indexed citations
2.
Nguyen, Huy A., Xinying Hou, John Stamper, & Bruce M. McLaren. (2020). Moving beyond Test Scores: Analyzing the Effectiveness of a Digital Learning Game through Learning Analytics.. Educational Data Mining.5 indexed citations
3.
Wang, Jingyu, et al.. (2019). Early Detection of Wheel Spinning: Comparison across Tutors, Models, Features, and Operationalizations.. Grantee Submission.1 indexed citations
4.
Nguyen, Huy A., Yeyu Wang, John Stamper, & Bruce M. McLaren. (2019). Using Knowledge Component Modeling to Increase Domain Understanding in a Digital Learning Game.. Educational Data Mining.6 indexed citations
5.
Stamper, John, et al.. (2018). Linkage Objects for Generalized Instruction in Coding (LOGIC).. The Florida AI Research Society. 443–446.2 indexed citations
6.
Eagle, Michael, Albert T. Corbett, John Stamper, & Bruce M. McLaren. (2018). Predicting Individualized Learner Models across Tutor Lessons.. Educational Data Mining.2 indexed citations
7.
Eagle, Michael, et al.. (2017). Automatic Peer Tutor Matching: Data-Driven Methods to Enable New Opportunities for Help.. Educational Data Mining.2 indexed citations
8.
Liu, Ran, Jodi L. Davenport, & John Stamper. (2016). Beyond Log Files: Using Multi-Modal Data Streams towards Data-Driven KC Model Improvement.. Educational Data Mining. 436–441.5 indexed citations
9.
Stamper, John, Zachary A. Pardos, Manolis Mavrikis, & Bruce M. McLaren. (2014). Proceedings of the Seventh International Conference on Educational Data Mining (EDM) (7th, London, United Kingdom, July 4-7, 2014).. Educational Data Mining.1 indexed citations
10.
Stamper, John, Kenneth R. Koedinger, & Elizabeth A. McLaughlin. (2013). A Comparison of Model Selection Metrics in DataShop.. Educational Data Mining. 284–287.7 indexed citations
11.
Williams, Joseph Jay, Alexander Renkl, Kenneth R. Koedinger, & John Stamper. (2013). Online Education: A Unique Opportunity for Cognitive Scientists to Integrate Research and Practice. Cognitive Science. 35(35).7 indexed citations
12.
Johnson, Matthew W., Michael Eagle, John Stamper, & Tiffany Barnes. (2013). An Algorithm for Reducing the Complexity of Interaction Networks.. Educational Data Mining. 248–251.4 indexed citations
13.
Stamper, John, et al.. (2012). The rise of the super experiment. Educational Data Mining. 2012(1). 196–200.13 indexed citations
14.
Koedinger, Kenneth R., Elizabeth A. McLaughlin, & John Stamper. (2012). Automated Student Model Improvement. Educational Data Mining. 2012(1). 17–24.55 indexed citations
15.
Pechenizkiy, Mykola, Toon Calders, Cristina Conati, et al.. (2011). Proceedings of the International Conference on Educational Data Mining (EDM) (4th, Eindhoven, the Netherlands, July 6-8, 2011).. Educational Data Mining.2 indexed citations
16.
Koedinger, Kenneth R., et al.. (2011). Avoiding Problem Selection Thrashing with Conjunctive Knowledge Tracing.. Educational Data Mining. 91–100.29 indexed citations
17.
Koedinger, Kenneth R. & John Stamper. (2010). A Data Driven Approach to the Discovery of Better Cognitive Models.. Educational Data Mining. 325–326.6 indexed citations
18.
Stamper, John, et al.. (2010). Using a Bayesian Knowledge Base for Hint Selection on Domain Specific Problems.. Educational Data Mining. 327–328.2 indexed citations
19.
Barnes, Tiffany & John Stamper. (2010). Automatic Hint Generation for Logic Proof Tutoring Using Historical Data. Educational Technology & Society. 13(1). 3–12.21 indexed citations
20.
Barnes, Tiffany, et al.. (2008). A pilot study on logic proof tutoring using hints generated from historical student data.. Educational Data Mining. 197–201.25 indexed citations
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.