I‐Chun Hung

16 papers receiving 570 citations

Peers

I‐Chun Hung
Comparison fields: 5 of 54
  • General Arts and Humanities 19
  • Computer Science Applications 48
  • Education 272
  • Information Systems 144
  • Human-Computer Interaction 40
Replace Seokmin Kang with:
Seokmin Kang United States
Erin Ottmar United States
João Filipe Matos Portugal
Wei‐Chieh Fang Taiwan
Chronis Kynigos Greece
Laurie D. Edwards United States
Xinhao Xu United States
Engin W. Bumbacher United States
Nico Rutten Netherlands
Joseph E. Michaelis United States
I‐Chun Hung relative to Seokmin Kang United States Seokmin Kang's profile →
Citations per field
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Citations per year

Countries citing papers authored by I‐Chun Hung

Since Specialization
Citations

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

Fields of papers citing papers by I‐Chun Hung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by I‐Chun Hung. 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 I‐Chun Hung. The network helps show where I‐Chun Hung may publish in the future.

Co-authors

The 10 scholars most cited alongside I‐Chun Hung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with I‐Chun Hung Line = papers co-authored together I‐Chun Hung links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 2010132
2 2017117
3
A Joyful Classroom Learning System with Robot Learning Companion for Children to Learn Mathematics Multiplication
201191
4 201365
5 201960
6 201250
7 201437
8 201521
9 201610
10 20106
11 20113
12 20143
13 20153
14 20193
15 20111
16 20121
17 20210
18 20220

About I‐Chun Hung

I‐Chun Hung is a scholar working on Experimental and Cognitive Psychology, Human-Computer Interaction, Information Systems, Education and Social Psychology, having authored 18 papers that have together received 603 indexed citations. Recurring topics across this work include Visual and Cognitive Learning Processes (5 papers), Social Robot Interaction and HRI (4 papers), Mobile Learning in Education (4 papers), Online and Blended Learning (3 papers), Educational Robotics and Engineering (3 papers), Intelligent Tutoring Systems and Adaptive Learning (2 papers), Virtual Reality Applications and Impacts (2 papers) and Innovative Teaching Methods (2 papers). The work is most often cited by research in General Arts and Humanities (19 citations), Computer Science Applications (48 citations), Education (272 citations), Information Systems (144 citations) and Human-Computer Interaction (40 citations). I‐Chun Hung has collaborated with scholars based in Taiwan, United States and Canada. Frequent co-authors include Nian‐Shing Chen, Kinshuk Kinshuk, Ling Lee, Chun‐Wang Wei, Wei‐Chieh Fang, Gwo‐Jen Hwang, Sie Wai Chew, Junfeng Yang, Echo Huang and Chun‐Chih Chen. Their work appears in journals such as Educational Technology Research and Development, Computers & Education, ˜The œturkish online journal of educational technology, Innovations in Education and Teaching International and Smart Learning Environments.

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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