Yanjin Long

418 citations
4 papers · 102 indexed · h-index 3
Topics
Innovative Teaching and Learning Methods (3 papers)Online Learning and Analytics (3 papers)Intelligent Tutoring Systems and Adaptive Learning (3 papers)
Partner nations
United StatesCanada

In The Last Decade

Yanjin Long

4 papers receiving 100 citations

Peers

Yanjin Long
Comparison fields: 5 of 23
  • Developmental and Educational Psychology 67
  • Computer Science Applications 65
  • Artificial Intelligence 49
  • Education 30
  • Information Systems 8
Replace Andrew Mabbott with:
Andrew Mabbott United Kingdom
Aysu Ezen-Can United States
Augusto Chioccariello Italy
Agnieszka Leńko‐Szymańska Poland
Deborah Butcher United States
Xinying Hou United States
Ana Isabel Sacristán Mexico
Christophe Reffay France
Serafeim A. Triantafyllou Greece
Kordula De Kuthy Germany
Yanjin Long relative to Andrew Mabbott United Kingdom Andrew Mabbott's profile →
Citations per field
00.5×
Andrew Mabbott · 1×
Citations per year

Countries citing papers authored by Yanjin Long

Since Specialization
Citations

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

Fields of papers citing papers by Yanjin Long

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yanjin Long

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

All Works

4 of 4 papers shown
#WorkIndexed citations
1 3
2 37
3
Redefining "What" in Analyses of Who Does What in MOOCs.
2
4 60

About Yanjin Long

Yanjin Long is a scholar working on Computer Science Applications, Developmental and Educational Psychology and Artificial Intelligence, having authored 4 papers that have together received 102 indexed citations. Recurring topics across this work include Innovative Teaching and Learning Methods (3 papers), Online Learning and Analytics (3 papers) and Intelligent Tutoring Systems and Adaptive Learning (3 papers). The work is most often cited by research in Computer Science Applications (65 citations), Developmental and Educational Psychology (67 citations) and Artificial Intelligence (49 citations). Yanjin Long has collaborated with scholars based in United States and Canada. Frequent co-authors include Vincent Aleven, Carrie Demmans Epp, Kenneth Holstein and Christian D. Schunn. Their work appears in journals such as ACM Transactions on Computer-Human Interaction, User Modeling and User-Adapted Interaction and Educational Data Mining.

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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