1.3k total citations 6 papers, 407 citations indexed
About
Hao Cen is a scholar working on Artificial Intelligence, Information Systems and Computer Science Applications.
According to data from OpenAlex, Hao Cen has authored 6 papers receiving a total of 407 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Information Systems and 2 papers in Computer Science Applications. Recurrent topics in Hao Cen's work include Intelligent Tutoring Systems and Adaptive Learning (6 papers), Educational Technology and Assessment (3 papers) and AI-based Problem Solving and Planning (3 papers). Hao Cen is often cited by papers focused on Intelligent Tutoring Systems and Adaptive Learning (6 papers), Educational Technology and Assessment (3 papers) and AI-based Problem Solving and Planning (3 papers). Hao Cen collaborates with scholars based in United States. Hao Cen's co-authors include Kenneth R. Koedinger, Philip I. Pavlik, Brian W. Junker, Jack Mostow, Joseph E. Beck and Evandro Gouvêa and has published in prestigious journals such as Educational Data Mining.
Citations per year, relative to Hao Cen Hao Cen (= 1×)
peers
José P. González-Brenes
Countries citing papers authored by Hao Cen
Since
Specialization
Citations
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).
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-authorship network of co-authors of Hao Cen
This figure shows the co-authorship network connecting the top 25 collaborators of Hao Cen.
A scholar is included among the top collaborators of Hao Cen 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 Hao Cen. Hao Cen is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
6 of 6 papers shown
1.
Pavlik, Philip I., Hao Cen, & Kenneth R. Koedinger. (2009). Learning Factors Transfer Analysis: Using Learning Curve Analysis to Automatically Generate Domain Models. Educational Data Mining. 2009(1). 121–130.46 indexed citations
2.
Koedinger, Kenneth R. & Hao Cen. (2009). Generalized learning factors analysis: improving cognitive models with machine learning.14 indexed citations
3.
Pavlik, Philip I., Hao Cen, & Kenneth R. Koedinger. (2009). Performance Factors Analysis --A New Alternative to Knowledge Tracing. 2009(1). 531–538.269 indexed citations
4.
Pavlik, Philip I., et al.. (2008). Using Item-type Performance Covariance to Improve the Skill Model of an Existing Tutor.. Educational Data Mining. 77–86.17 indexed citations
5.
Cen, Hao, Kenneth R. Koedinger, & Brian W. Junker. (2007). Is Over Practice Necessary? --Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining. 511–518.60 indexed citations
6.
Mostow, Jack, et al.. (2005). Interactive Demonstration of a Generic Tool to Browse Tutor-Student Interactions.1 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.