Meehyun Yoon

972 total citations
20 papers, 660 citations indexed

About

Meehyun Yoon is a scholar working on Education, Computer Science Applications and Developmental and Educational Psychology. According to data from OpenAlex, Meehyun Yoon has authored 20 papers receiving a total of 660 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Education, 11 papers in Computer Science Applications and 11 papers in Developmental and Educational Psychology. Recurrent topics in Meehyun Yoon's work include Innovative Teaching and Learning Methods (10 papers), Online Learning and Analytics (10 papers) and Online and Blended Learning (10 papers). Meehyun Yoon is often cited by papers focused on Innovative Teaching and Learning Methods (10 papers), Online Learning and Analytics (10 papers) and Online and Blended Learning (10 papers). Meehyun Yoon collaborates with scholars based in South Korea and United States. Meehyun Yoon's co-authors include Il‐Hyun Jo, Dongho Kim, Eulho Jung, Jungeun Lee, Dongho Kim, Sanghoon Park, Robert Maribe Branch, Yeonjeong Park, Janette R. Hill and Barbara Oakley and has published in prestigious journals such as Computers & Education, The Internet and Higher Education and Journal of Computer Assisted Learning.

In The Last Decade

Meehyun Yoon

17 papers receiving 624 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Meehyun Yoon South Korea 11 411 390 224 100 86 20 660
Il‐Hyun Jo South Korea 14 503 1.2× 447 1.1× 238 1.1× 158 1.6× 114 1.3× 53 802
Rianne Conijn Netherlands 11 417 1.0× 351 0.9× 188 0.8× 135 1.4× 171 2.0× 29 784
Hengtao Tang United States 18 463 1.1× 327 0.8× 242 1.1× 87 0.9× 93 1.1× 60 766
Alexander Whitelock‐Wainwright Australia 14 454 1.1× 316 0.8× 178 0.8× 122 1.2× 133 1.5× 23 740
Paula De Barba Australia 12 758 1.8× 454 1.2× 228 1.0× 119 1.2× 156 1.8× 31 1.0k
Chengyuan Jia Hong Kong 10 223 0.5× 391 1.0× 158 0.7× 159 1.6× 130 1.5× 16 717
Erkan Er Spain 13 266 0.6× 303 0.8× 254 1.1× 85 0.8× 77 0.9× 35 587
Tal Soffer Israel 12 279 0.7× 371 1.0× 112 0.5× 115 1.1× 58 0.7× 20 600
Hercy N.H. Cheng Taiwan 14 181 0.4× 285 0.7× 261 1.2× 129 1.3× 94 1.1× 49 627
Kristen E. DiCerbo United States 12 206 0.5× 234 0.6× 250 1.1× 77 0.8× 134 1.6× 35 590

Countries citing papers authored by Meehyun Yoon

Since Specialization
Citations

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

Fields of papers citing papers by Meehyun Yoon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Meehyun Yoon

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

All Works

20 of 20 papers shown
1.
Ding, Lu, et al.. (2024). Dialogue alongside or within lecturing videos for teaching debugging. Journal of Research on Technology in Education. 58(2). 250–267.
2.
Yoon, Meehyun, et al.. (2023). Task type matters: The impact of virtual reality training on training performance. Journal of Computer Assisted Learning. 40(1). 205–218. 9 indexed citations
3.
Ding, Lu, et al.. (2023). Unveiling the pedagogical advantage of tutoring‐style videos in an authentic biology class. Journal of Computer Assisted Learning. 40(3). 946–959. 2 indexed citations
4.
Yoon, Meehyun, et al.. (2022). Effects of Segmentation and Self-Explanation Designs on Cognitive Load in Instructional Videos. Contemporary Educational Technology. 14(2). ep347–ep347. 8 indexed citations
5.
Branch, Robert Maribe, Lu Ding, Eulho Jung, et al.. (2022). The combination of segmentation and self-explanation to enhance video-based learning. Active Learning in Higher Education. 25(2). 285–302. 3 indexed citations
6.
7.
Yoon, Meehyun, et al.. (2021). Relationships between adolescent smartphone usage patterns, achievement goals, and academic achievement. Asia Pacific Education Review. 24(1). 13–23. 20 indexed citations
8.
Yoon, Meehyun, Jungeun Lee, & Il‐Hyun Jo. (2021). Video learning analytics: Investigating behavioral patterns and learner clusters in video-based online learning. The Internet and Higher Education. 50. 100806–100806. 97 indexed citations
9.
Yoon, Meehyun, et al.. (2021). A structural relationship of perfectionism, self-efficacy, academic procrastination, and adaptation to college life. Korean Association For Learner-Centered Curriculum And Instruction. 21(13). 321–334.
10.
Yoon, Meehyun, Janette R. Hill, & Dongho Kim. (2021). Designing supports for promoting self-regulated learning in the flipped classroom. Journal of Computing in Higher Education. 33(2). 398–418. 61 indexed citations
11.
12.
Park, Sanghoon, et al.. (2020). The influence of academic level and course delivery mode on the use of motivational regulation strategies and learning engagement. Australasian Journal of Educational Technology. 36(3). 89–103. 11 indexed citations
13.
Yoon, Meehyun, et al.. (2018). Analyzing the log patterns of adult learners in LMS using learning analytics. 183. 1 indexed citations
14.
Jung, Eulho, Dongho Kim, Meehyun Yoon, Sanghoon Park, & Barbara Oakley. (2018). The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education. 128. 377–388. 80 indexed citations
15.
Kim, Dongho, Meehyun Yoon, Il‐Hyun Jo, & Robert Maribe Branch. (2018). Learning analytics to support self-regulated learning in asynchronous online courses: A case study at a women's university in South Korea. Computers & Education. 127. 233–251. 112 indexed citations
16.
Park, Yeonjeong, et al.. (2016). Evaluation of Online Log Variables that Estimate Learners’ Time Management in a Korean Online Learning Context. The International Review of Research in Open and Distributed Learning. 17(1). 22 indexed citations
17.
Kim, Dongho, Yeonjeong Park, Meehyun Yoon, & Il‐Hyun Jo. (2016). Toward evidence-based learning analytics: Using proxy variables to improve asynchronous online discussion environments. The Internet and Higher Education. 30. 30–43. 74 indexed citations
18.
Kim, Dongho, et al.. (2015). Constructing Proxy Variables to Measure Adult Learners' Time Management Strategies in LMS.. Educational Technology & Society. 18(3). 214–225. 39 indexed citations
19.
Yoon, Meehyun, et al.. (2014). Effects of Communication Competence and Social Network Centralities on Learner Performance. Educational Technology & Society. 17(3). 108–120. 6 indexed citations
20.
Jo, Il‐Hyun, Dongho Kim, & Meehyun Yoon. (2014). Analyzing the log patterns of adult learners in LMS using learning analytics. 183–187. 34 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Il‐Hyun Jo Breakdown of academic impact, for papers by Rianne Conijn Breakdown of academic impact, for papers by Hengtao Tang Breakdown of academic impact, for papers by Alexander Whitelock‐Wainwright Breakdown of academic impact, for papers by Paula De Barba Breakdown of academic impact, for papers by Chengyuan Jia Breakdown of academic impact, for papers by Erkan Er Breakdown of academic impact, for papers by Tal Soffer Breakdown of academic impact, for papers by Hercy N.H. Cheng Breakdown of academic impact, for papers by Kristen E. DiCerbo
Rankless by CCL
2026