Yong Han

1.7k citations
18 papers · 82 indexed · h-index 6
Topics
Online Learning and Analytics (7 papers)Intelligent Tutoring Systems and Adaptive Learning (5 papers)Topic Modeling (2 papers)
Partner nations
ChinaJapan

In The Last Decade

Yong Han

16 papers receiving 75 citations

Peers

Yong Han
Comparison fields: 5 of 45
  • Artificial Intelligence 32
  • Computer Science Applications 25
  • Management Science and Operations Research 20
  • Electrical and Electronic Engineering 10
  • Information Systems 9
Replace Xin Rong with:
Xin Rong United States
Hongbo Zhang China
Maria Maistro Italy
Djoko Saryono Indonesia
Masoud Mansoury Netherlands
Anne-Marie Vercoustre Australia
Thomas Riechert Germany
Pavlos Vougiouklis United Kingdom
Ashudeep Singh United States
Xiaoyong Jin United States
Yong Han relative to Xin Rong United States Xin Rong's profile →
Citations per field
00.5×5.5×
Xin Rong · 1×
Citations per year

Countries citing papers authored by Yong Han

Since Specialization
Citations

This map shows the geographic impact of Yong Han'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 Yong Han with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yong Han more than expected).

Fields of papers citing papers by Yong Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yong Han. 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 Yong Han. The network helps show where Yong Han may publish in the future.

Co-authorship network of co-authors of Yong Han

This figure shows the co-authorship network connecting the top 25 collaborators of Yong Han. A scholar is included among the top collaborators of Yong Han 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 Yong Han. Yong Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
#WorkIndexed citations
1 0
2 23
3 5
4 3
5 2
6 9
7 1
8
A Human-Machine Hybrid Peer Grading Framework for SPOCs.
1
9
FIRE2019@AILA: Legal Information Retrieval Using Improved BM25.
3
10 2
11
Improving Models of Peer Grading in SPOC.
2
12 8
13 5
14
Source Retrieval and Text Alignment Corpus Construction for Plagiarism Detection.
1
15 2
16 11
17 2
18 2

About Yong Han

Yong Han is a scholar working on Computer Science Applications, Computer Graphics and Computer-Aided Design and Artificial Intelligence, having authored 18 papers that have together received 82 indexed citations. Recurring topics across this work include Online Learning and Analytics (7 papers), Intelligent Tutoring Systems and Adaptive Learning (5 papers) and Topic Modeling (2 papers). The work is most often cited by research in Computer Science Applications (25 citations), Management Science and Operations Research (20 citations) and Artificial Intelligence (32 citations). Yong Han has collaborated with scholars based in China and Japan. Frequent co-authors include Wenjun Wu, Zhenghui Hu, Ge Chen, Shanshan Wang, Chunyong Ma, Yong Chen, Xuan Zhou, Huafeng Wang, Tingting Han and Zhongyuan Han. Their work appears in journals such as IEEE Access, Applied Sciences and Applied Intelligence.

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.

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