Jaechoon Jo

525 citations
42 papers · 386 · h-index 10

Impact in

Papers in

    • Education and Learning Interventions 10
    • Educational Systems and Policies 6
    • Recommender Systems and Techniques 4
    • Technology and Data Analysis 3
    • Advanced Text Analysis Techniques 5
    • Topic Modeling 4

Jaechoon Jo

36 papers receiving 361 citations

Peers

Jaechoon Jo
Comparison fields: 5 of 74
  • Computer Science Applications 65
  • Information Systems 154
  • Developmental and Educational Psychology 51
  • Artificial Intelligence 107
  • Computer Vision and Pattern Recognition 61
Replace Mukta Goyal with:
Mukta Goyal India
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David E. Millard United Kingdom
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Jaechoon Jo relative to Mukta Goyal India Mukta Goyal's profile →
Citations per field
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Citations per year

Countries citing papers authored by Jaechoon Jo

Since Specialization
Citations

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

Fields of papers citing papers by Jaechoon Jo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 15 scholars most cited alongside Jaechoon Jo, 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 Jaechoon Jo Line = papers co-authored together Jaechoon Jo links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 42 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201859
2 202056
3 201742
4 202033
5 201524
6 201422
7 201618
8 201517
9 201813
10 201511
11 20208
12 20218
13 20208
14 20187
15 20196
16 20176
17
A Conceptual Model of Smart Education Considering Teaching-Learning Activities and Learner's Characteristics
20125
18 20175
19 20214
20 20203

About Jaechoon Jo

Jaechoon Jo is a scholar working on Information Systems, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications and Education, having authored 42 papers that have together received 386 indexed citations. Recurring topics across this work include Education and Learning Interventions (10 papers), Online Learning and Analytics (7 papers), Educational Systems and Policies (6 papers), Advanced Text Analysis Techniques (5 papers), Topic Modeling (4 papers), Recommender Systems and Techniques (4 papers), Technology and Data Analysis (3 papers) and Video Analysis and Summarization (3 papers). The work is most often cited by research in Computer Science Applications (65 citations), Information Systems (154 citations), Developmental and Educational Psychology (51 citations), Artificial Intelligence (107 citations) and Computer Vision and Pattern Recognition (61 citations). Jaechoon Jo has collaborated with scholars based in South Korea, Japan and Estonia. Frequent co-authors include Heuiseok Lim, Chanhee Lee, Danial Hooshyar, Gyeongmin Kim, Yeongwook Yang, Seolhwa Lee, Kinam Park, Kinam Park, Chanjun Park and Kyu Han Koh. Their work appears in journals such as Wireless Personal Communications, Personal and Ubiquitous Computing, Information Technology and Management, Electronics and Journal of Ambient Intelligence and Humanized Computing.

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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