467 total citations 20 papers, 258 citations indexed
About
Feng-Jen Yang is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics.
According to data from OpenAlex, Feng-Jen Yang has authored 20 papers receiving a total of 258 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 6 papers in Information Systems and 4 papers in Computational Theory and Mathematics. Recurrent topics in Feng-Jen Yang's work include Intelligent Tutoring Systems and Adaptive Learning (5 papers), Speech and dialogue systems (4 papers) and Natural Language Processing Techniques (4 papers). Feng-Jen Yang is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (5 papers), Speech and dialogue systems (4 papers) and Natural Language Processing Techniques (4 papers). Feng-Jen Yang collaborates with scholars based in United States. Feng-Jen Yang's co-authors include Michael Glass, Martha Evens, Jung Hee Kim, Tyler R. Simpson, Yujian Zhou, Reva Freedman, Sesha S. Srinivasan and Wei Ding and has published in prestigious journals such as ACM Inroads, The Florida AI Research Society and ACM SIGCSE Bulletin.
In The Last Decade
Feng-Jen Yang
17 papers
receiving
231 citations
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Feng-Jen Yang'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 Feng-Jen Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Feng-Jen Yang more than expected).
This network shows the impact of papers produced by Feng-Jen Yang. 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 Feng-Jen Yang. The network helps show where Feng-Jen Yang may publish in the future.
Co-authorship network of co-authors of Feng-Jen Yang
This figure shows the co-authorship network connecting the top 25 collaborators of Feng-Jen Yang.
A scholar is included among the top collaborators of Feng-Jen Yang 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 Feng-Jen Yang. Feng-Jen Yang is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Yang, Feng-Jen. (2010). An Intelligent Tutoring System for Relational Database Schema Normalization.. International Conference on Artificial Intelligence. 494–498.1 indexed citations
Yang, Feng-Jen. (2004). The Domino Effect and Linear Recursion.. 201–206.2 indexed citations
17.
Evens, Martha & Feng-Jen Yang. (2001). Turn planning for a dialogue-based intelligent tutoring system.
18.
Freedman, Reva, Michael Glass, Yujian Zhou, et al.. (2001). Adaptive Processing in a Medical Intelligent Tutoring System.4 indexed citations
19.
Yang, Feng-Jen, Michael Glass, & Martha Evens. (2001). Evaluation of the Turn Planner in CIRCSIM-Tutor.1 indexed citations
20.
Yang, Feng-Jen, Jung Hee Kim, Michael Glass, & Martha Evens. (2000). Turn Planning in CIRCSIM-Tutor. The Florida AI Research Society. 60–64.8 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.