Lele Sha

966 total citations · 1 hit paper
15 papers, 452 citations indexed

About

Lele Sha is a scholar working on Artificial Intelligence, Computer Science Applications and Developmental and Educational Psychology. According to data from OpenAlex, Lele Sha has authored 15 papers receiving a total of 452 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 9 papers in Computer Science Applications and 3 papers in Developmental and Educational Psychology. Recurrent topics in Lele Sha's work include Online Learning and Analytics (7 papers), Topic Modeling (5 papers) and Innovative Teaching and Learning Methods (3 papers). Lele Sha is often cited by papers focused on Online Learning and Analytics (7 papers), Topic Modeling (5 papers) and Innovative Teaching and Learning Methods (3 papers). Lele Sha collaborates with scholars based in Australia, Saudi Arabia and United Kingdom. Lele Sha's co-authors include Dragan Gašević, Guanliang Chen, Yuheng Li, Lixiang Yan, Xinyu Li, Roberto Martínez‐Maldonado, Linxuan Zhao, Yueqiao Jin, Mladen Raković and Jionghao Lin and has published in prestigious journals such as Expert Systems with Applications, Future Generation Computer Systems and British Journal of Educational Technology.

In The Last Decade

Lele Sha

13 papers receiving 433 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Practical and ethical challenges of large language models in education: A systematic scoping review

2023 288 citations Lixiang Yan, Lele Sha et al. British Journal of Educational Technology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Lele Sha Australia	8	246	159	127	60	50	15	452
Yueqiao Jin Australia	7	195 0.8×	161 1.0×	118 0.9×	53 0.9×	50 1.0×	15	472
Linxuan Zhao Australia	11	209 0.8×	195 1.2×	103 0.8×	45 0.8×	110 2.2×	33	568
Mehmet Haldun Kaya Türkiye	4	198 0.8×	181 1.1×	193 1.5×	45 0.8×	44 0.9×	9	471
Xinyu Li Australia	11	247 1.0×	244 1.5×	119 0.9×	41 0.7×	144 2.9×	43	655
Bahar Memarian Canada	7	118 0.5×	82 0.5×	110 0.9×	64 1.1×	24 0.5×	22	302
Amr M. Mohamed Saudi Arabia	6	154 0.6×	123 0.8×	106 0.8×	15 0.3×	59 1.2×	21	349
Maya Bialik	2	156 0.6×	236 1.5×	74 0.6×	43 0.7×	59 1.2×	3	474
Ayşe Aslan United Kingdom	4	136 0.6×	163 1.0×	87 0.7×	33 0.6×	34 0.7×	14	414
Duane Searsmith United States	6	143 0.6×	158 1.0×	77 0.6×	26 0.4×	45 0.9×	17	368
Leo S. Lo United States	10	155 0.6×	85 0.5×	96 0.8×	61 1.0×	33 0.7×	28	393

Countries citing papers authored by Lele Sha

Since Specialization

Citations

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

Fields of papers citing papers by Lele Sha

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lele Sha

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

All Works

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15 of 15 papers shown

Fernandez-Nieto, Gloria Milena, et al.. (2026). Capturing and sharing know-how through visual process representations: a human-Centred approach to teacher workflows. Behaviour and Information Technology. 1–30.

Sha, Lele, Gloria Milena Fernandez-Nieto, Yuheng Li, et al.. (2025). ShareFlows: Seamless Knowledge Capture and Proactive Push for Efficient Teacher Workflows in Higher Education. Monash University Research Portal (Monash University). 1242–1255.

Liu, Sannyuya, et al.. (2025). Annotation Guideline-Based Knowledge Augmentation: Toward Enhancing Large Language Models for Educational Text Classification. IEEE Transactions on Learning Technologies. 18. 619–634. 1 indexed citations

Zhao, Linxuan, Dragan Gašević, Zachari Swiecki, et al.. (2024). Towards automated transcribing and coding of embodied teamwork communication through multimodal learning analytics. British Journal of Educational Technology. 55(4). 1673–1702. 7 indexed citations

Sha, Lele, et al.. (2024). Towards Automatic Boundary Detection for Human-AI Collaborative Hybrid Essay in Education. Proceedings of the AAAI Conference on Artificial Intelligence. 38(20). 22502–22510. 8 indexed citations

Fernandez-Nieto, Gloria Milena, Zachari Swiecki, Yi‐Shan Tsai, et al.. (2024). Co-designing a knowledge management tool for educator communities of practice. Designing Interactive Systems Conference. 1970–1990. 3 indexed citations

Yan, Lixiang, Lele Sha, Linxuan Zhao, et al.. (2023). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology. 55(1). 90–112. 288 indexed citations breakdown →

Li, Yuheng, Lele Sha, Lixiang Yan, et al.. (2023). Can large language models write reflectively. Computers and Education Artificial Intelligence. 4. 100140–100140. 53 indexed citations

Sha, Lele, Dragan Gašević, & Guanliang Chen. (2023). Lessons from debiasing data for fair and accurate predictive modeling in education. Expert Systems with Applications. 228. 120323–120323. 9 indexed citations

10.

Sha, Lele, Yuheng Li, Mladen Raković, et al.. (2023). Moral Machines or Tyranny of the Majority? A Systematic Review on Predictive Bias in Education. 499–508. 16 indexed citations

11.

Sha, Lele, et al.. (2022). Leveraging Class Balancing Techniques to Alleviate Algorithmic Bias for Predictive Tasks in Education. IEEE Transactions on Learning Technologies. 15(4). 481–492. 36 indexed citations

12.

Sha, Lele, Mladen Raković, Jionghao Lin, et al.. (2022). Is the Latest the Greatest? A Comparative Study of Automatic Approaches for Classifying Educational Forum Posts. IEEE Transactions on Learning Technologies. 16(3). 339–352. 9 indexed citations

13.

Sha, Lele, Dragan Gašević, & Guanliang Chen. (2022). Lessons from Debiasing Data for Fair and Accurate Predictive Modeling in Education. SSRN Electronic Journal. 2 indexed citations

14.

Sha, Lele, Mladen Raković, Alexander Whitelock‐Wainwright, et al.. (2021). Which Hammer should I Use? A Systematic Evaluation of Approaches for Classifying Educational Forum Posts. Monash University Research Portal (Monash University). 7 indexed citations

15.

Lin, Jionghao, Shaveen Singh, Lele Sha, et al.. (2021). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems. 127. 194–207. 13 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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