Lyn Lim

682 total citations · 1 hit paper
18 papers, 437 citations indexed

About

Lyn Lim is a scholar working on Developmental and Educational Psychology, Computer Science Applications and Education. According to data from OpenAlex, Lyn Lim has authored 18 papers receiving a total of 437 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Developmental and Educational Psychology, 15 papers in Computer Science Applications and 9 papers in Education. Recurrent topics in Lyn Lim's work include Innovative Teaching and Learning Methods (17 papers), Online Learning and Analytics (15 papers) and Online and Blended Learning (8 papers). Lyn Lim is often cited by papers focused on Innovative Teaching and Learning Methods (17 papers), Online Learning and Analytics (15 papers) and Online and Blended Learning (8 papers). Lyn Lim collaborates with scholars based in Germany, Australia and Netherlands. Lyn Lim's co-authors include Inge Molenaar, Joep van der Graaf, Yizhou Fan, Maria Bannert, Dragan Gašević, Johanna D. Moore, Mladen Raković, Shaveen Singh, Jonathan Kilgour and Tongguang Li and has published in prestigious journals such as Computers in Human Behavior, Frontiers in Psychology and Educational Psychology Review.

In The Last Decade

Lyn Lim

17 papers receiving 425 citations

Hit Papers

Effects of real-time analytics-based personalized scaffol... 2022 2026 2023 2024 2022 25 50 75 100

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lyn Lim Germany 13 304 284 177 109 37 18 437
Lu Zhong China 9 178 0.6× 214 0.8× 122 0.7× 85 0.8× 82 2.2× 12 376
Jiayu Niu China 8 165 0.5× 194 0.7× 107 0.6× 77 0.7× 64 1.7× 9 332
Melody Siadaty Canada 8 204 0.7× 186 0.7× 125 0.7× 53 0.5× 33 0.9× 14 308
Xiaoshan Huang Canada 8 87 0.3× 78 0.3× 87 0.5× 77 0.7× 37 1.0× 27 280
Daeyeoul Lee United States 6 132 0.4× 157 0.6× 164 0.9× 28 0.3× 56 1.5× 8 289
Amber Chauncey United States 4 218 0.7× 97 0.3× 111 0.6× 86 0.8× 23 0.6× 6 277
Jim Ranalli United States 10 306 1.0× 52 0.2× 257 1.5× 123 1.1× 74 2.0× 15 591
Thieme Hennis Netherlands 6 123 0.4× 216 0.8× 191 1.1× 63 0.6× 39 1.1× 11 335
Carolin Hahnel Germany 11 176 0.6× 59 0.2× 135 0.8× 67 0.6× 62 1.7× 34 352
Yangyu Xiao China 9 87 0.3× 63 0.2× 174 1.0× 80 0.7× 51 1.4× 17 358

Countries citing papers authored by Lyn Lim

Since Specialization
Citations

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

Fields of papers citing papers by Lyn Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lyn Lim

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

All Works

18 of 18 papers shown
1.
Fan, Yizhou, Tongguang Li, Mladen Raković, et al.. (2025). FLoRA Engine. Journal of Learning Analytics. 12(1). 391–413. 2 indexed citations
2.
Lämsä, Joni, Lyn Lim, Tongguang Li, et al.. (2025). A Systematic Review of Self-Regulated Learning through Integration of Multimodal Data and Artificial Intelligence. Educational Psychology Review. 37(2). 2 indexed citations
3.
Raković, Mladen, Xinyu Li, Joni Lämsä, et al.. (2025). Using Trace Data of Secondary Students to Understand Metacognitive Processes in Writing From Multiple Sources. Journal of Computer Assisted Learning. 41(5).
4.
Chen, Guanliang, Yizhou Fan, Mladen Raković, et al.. (2024). Towards prescriptive analytics of self‐regulated learning strategies: A reinforcement learning approach. British Journal of Educational Technology. 55(4). 1747–1771. 4 indexed citations
5.
Graaf, Joep van der, Mladen Raković, Yizhou Fan, et al.. (2023). How to design and evaluate personalized scaffolds for self-regulated learning. Metacognition and Learning. 18(3). 783–810. 16 indexed citations
6.
Li, Tongguang, Yizhou Fan, Yeyu Wang, et al.. (2023). Analytics of self-regulated learning scaffolding: effects on learning processes. Frontiers in Psychology. 14. 1206696–1206696. 22 indexed citations
7.
Fan, Yizhou, Mladen Raković, Joep van der Graaf, et al.. (2023). Towards a fuller picture: Triangulation and integration of the measurement of self‐regulated learning based on trace and think aloud data. Journal of Computer Assisted Learning. 39(4). 1303–1324. 21 indexed citations
8.
Lim, Lyn, Maria Bannert, Joep van der Graaf, et al.. (2023). How do students learn with real‐time personalized scaffolds?. British Journal of Educational Technology. 55(4). 1309–1327. 20 indexed citations
9.
Fan, Yizhou, Lyn Lim, Joep van der Graaf, et al.. (2022). Improving the measurement of self-regulated learning using multi-channel data. Metacognition and Learning. 17(3). 1025–1055. 42 indexed citations
10.
Raković, Mladen, Yizhou Fan, Joep van der Graaf, et al.. (2022). Using Learner Trace Data to Understand Metacognitive Processes in Writing from Multiple Sources. mediaTUM (Technical University of Munich). 130–141. 15 indexed citations
11.
Raković, Mladen, Tongguang Li, Yizhou Fan, et al.. (2022). Harnessing the potential of trace data and linguistic analysis to predict learner performance in a multi‐text writing task. Journal of Computer Assisted Learning. 39(3). 703–718. 23 indexed citations
12.
Fan, Yizhou, Joep van der Graaf, Lyn Lim, et al.. (2022). Towards investigating the validity of measurement of self-regulated learning based on trace data. Metacognition and Learning. 17(3). 949–987. 52 indexed citations
13.
Lim, Lyn, Maria Bannert, Joep van der Graaf, et al.. (2022). Effects of real-time analytics-based personalized scaffolds on students’ self-regulated learning. Computers in Human Behavior. 139. 107547–107547. 104 indexed citations breakdown →
14.
Graaf, Joep van der, Lyn Lim, Yizhou Fan, et al.. (2022). The Dynamics Between Self-Regulated Learning and Learning Outcomes: an Exploratory Approach and Implications. Metacognition and Learning. 17(3). 745–771. 33 indexed citations
15.
Srivastava, Namrata, Yizhou Fan, Mladen Raković, et al.. (2022). Effects of Internal and External Conditions on Strategies of Self-regulated Learning: A Learning Analytics Study. mediaTUM (Technical University of Munich). 392–403. 27 indexed citations
16.
Lim, Lyn, Maria Bannert, Joep van der Graaf, et al.. (2021). Temporal Assessment of Self-Regulated Learning by Mining Students’ Think-Aloud Protocols. Frontiers in Psychology. 12. 749749–749749. 23 indexed citations
17.
Graaf, Joep van der, Lyn Lim, Yizhou Fan, et al.. (2021). Do Instrumentation Tools Capture Self-Regulated Learning?. mediaTUM (Technical University of Munich). 438–448. 30 indexed citations
18.
Graaf, Joep van der, Inge Molenaar, Lyn Lim, et al.. (2020). Facilitating self-regulated learning with personalized scaffolds on student's own regulation activities. Monash University Research Portal (Monash University). 46–48. 1 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.

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