Soojin Jun

495 citations
47 papers · 292 · h-index 9

Impact in

Papers in

    • Education and Learning Interventions 16
    • Educational Systems and Policies 13
    • Social Robot Interaction and HRI 6
    • Human-Automation Interaction and Safety 4

Soojin Jun

29 papers receiving 261 citations

Peers

Soojin Jun
Comparison fields: 5 of 64
  • Computer Science Applications 64
  • Human-Computer Interaction 46
  • Occupational Therapy 17
  • Human Factors and Ergonomics 8
  • Developmental and Educational Psychology 38
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Citations per year

Countries citing papers authored by Soojin Jun

Since Specialization
Citations

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

Fields of papers citing papers by Soojin Jun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Soojin Jun, 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 Soojin Jun Line = papers co-authored together Soojin Jun links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201650
2 202037
3 202332
4 200630
5 201522
6 201813
7 20209
8
Online Pair-Programming for Learning Programming of Novices
20078
9 20218
10 20217
11 20177
12 20117
13 20146
14
Design Guidelines and Recommendations for In-Vehicle Navigation Systems
20105
15 20205
16 20245
17 20235
18 20144
19 20173
20 20243

About Soojin Jun

Soojin Jun is a scholar working on Information Systems, Social Psychology, Computer Networks and Communications, Human-Computer Interaction and Sociology and Political Science, having authored 47 papers that have together received 292 indexed citations. Recurring topics across this work include Education and Learning Interventions (16 papers), Educational Systems and Policies (13 papers), Educational Research and Pedagogy (9 papers), AI in Service Interactions (6 papers), Social Robot Interaction and HRI (6 papers), Human-Automation Interaction and Safety (4 papers), Ethics and Social Impacts of AI (4 papers) and Diverse Approaches in Healthcare and Education Studies (3 papers). The work is most often cited by research in Computer Science Applications (64 citations), Human-Computer Interaction (46 citations), Occupational Therapy (17 citations), Human Factors and Ergonomics (8 citations) and Developmental and Educational Psychology (38 citations). Soojin Jun has collaborated with scholars based in South Korea, United States and Switzerland. Frequent co-authors include Joonhwan Lee, Hyunmin Kang, Jodi Forlizzi, Scott E. Hudson, Dong-Hoon Shin, Seokwoo Song, Jinwoo Kim, Jisoo Park, Sangmi Kim and Seungbum Kim. Their work appears in journals such as Archives of Design Research, International Journal of Human-Computer Interaction, Behaviour and Information Technology, Computers in Human Behavior and Human-Computer Interaction.

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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