Countries citing papers authored by Noboru Matsuda
Since
Specialization
Citations
This map shows the geographic impact of Noboru Matsuda'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 Noboru Matsuda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Noboru Matsuda more than expected).
This network shows the impact of papers produced by Noboru Matsuda. 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 Noboru Matsuda. The network helps show where Noboru Matsuda may publish in the future.
Co-authorship network of co-authors of Noboru Matsuda
This figure shows the co-authorship network connecting the top 25 collaborators of Noboru Matsuda.
A scholar is included among the top collaborators of Noboru Matsuda 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 Noboru Matsuda. Noboru Matsuda is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Matsuda, Noboru, et al.. (2020). Learning a Policy Primes Quality Control: Towards Evidence-Based Automation of Learning Engineering.. Educational Data Mining.2 indexed citations
Matsuda, Noboru, et al.. (2016). How quickly can wheel spinning be detected. Educational Data Mining. 607–608.8 indexed citations
7.
Koedinger, Kenneth R., et al.. (2015). Methods for Evaluating Simulated Learners: Examples from SimStudent..6 indexed citations
8.
Matsuda, Noboru, et al.. (2015). Machine Beats Experts: Automatic Discovery of Skill Models for Data-Driven Online Course Refinement.. Educational Data Mining. 101–108.10 indexed citations
9.
Harpstead, Erik, et al.. (2015). Authoring Tutors with Complex Solutions: A Comparative Analysis of Example Tracing and SimStudent..4 indexed citations
Matsuda, Noboru, et al.. (2013). Toward a reflective SimStudent: Using experience to avoid generalization errors..1 indexed citations
14.
Matsuda, Noboru, et al.. (2012). Shallow learning as a pathway for successful learning both for tutors and tutees. Cognitive Science. 34(34).3 indexed citations
15.
Cohen, William W., et al.. (2011). A Machine Learning Approach for Automatic Student Model Discovery. Educational Data Mining. 31–40.65 indexed citations
16.
Li, Nan, Noboru Matsuda, William W. Cohen, & Kenneth R. Koedinger. (2010). Towards a Computational Model of Why Some Students Learn Faster than Others. National Conference on Artificial Intelligence.1 indexed citations
17.
Cohen, William W., Kenneth R. Koedinger, Andrew Lee, & Noboru Matsuda. (2009). A Computational Model of How Learner Errors Arise from Weak Prior Knowledge. eScholarship (California Digital Library). 31(31).18 indexed citations
18.
Matsuda, Noboru & Toshio Okamoto. (1994). Student Modelling for Procedural Problem Solving. IEICE Transactions on Information and Systems. 49–56.1 indexed citations
Tsukamura, Michio, et al.. (1973). A STUDY ON THE FREQUENCY OF 'ATYPICAL' MYCOBACTERIA AND OF 'ATYPICAL' MYCOBACTERIOSES IN JAPANESE NATIONAL CHEST HOSPITALS. 48(5). 203–211.
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.