Michael Liut

499 total citations
48 papers, 220 citations indexed

About

Michael Liut is a scholar working on Computer Science Applications, Artificial Intelligence and Developmental and Educational Psychology. According to data from OpenAlex, Michael Liut has authored 48 papers receiving a total of 220 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Science Applications, 14 papers in Artificial Intelligence and 13 papers in Developmental and Educational Psychology. Recurrent topics in Michael Liut's work include Online Learning and Analytics (14 papers), Teaching and Learning Programming (14 papers) and Innovative Teaching and Learning Methods (9 papers). Michael Liut is often cited by papers focused on Online Learning and Analytics (14 papers), Teaching and Learning Programming (14 papers) and Innovative Teaching and Learning Methods (9 papers). Michael Liut collaborates with scholars based in Canada, United States and Netherlands. Michael Liut's co-authors include Andrew Petersen, Oscar Karnalim, Ali Raza, Anna Ly, Joseph Jay Williams, Bogdan Simion, Jack Parkinson, Quintin Cutts, Furkan Alaca and Judy Sheard and has published in prestigious journals such as ACM SIGMOD Record, Proceedings of the ACM on Human-Computer Interaction and Complex & Intelligent Systems.

In The Last Decade

Michael Liut

34 papers receiving 214 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Liut Canada 10 63 59 36 27 26 48 220
Stephanie Ludi United States 11 56 0.9× 68 1.2× 110 3.1× 19 0.7× 37 1.4× 24 290
Tania Di Mascio Italy 10 23 0.4× 38 0.6× 59 1.6× 70 2.6× 55 2.1× 60 284
Alwyn Vwen Yen Lee Singapore 9 112 1.8× 61 1.0× 30 0.8× 57 2.1× 80 3.1× 21 217
Gloria Milena Fernandez-Nieto Australia 11 119 1.9× 65 1.1× 36 1.0× 74 2.7× 87 3.3× 19 284
Aisha Alsobhi Saudi Arabia 9 76 1.2× 56 0.9× 54 1.5× 83 3.1× 52 2.0× 20 241
Aidan Jones United Kingdom 6 90 1.4× 141 2.4× 30 0.8× 29 1.1× 51 2.0× 10 292
Nasrin Dehbozorgi United States 10 101 1.6× 78 1.3× 37 1.0× 45 1.7× 38 1.5× 32 242
Achraf Othman Qatar 10 23 0.4× 80 1.4× 26 0.7× 18 0.7× 74 2.8× 48 322
Dan Sun China 9 131 2.1× 53 0.9× 43 1.2× 30 1.1× 67 2.6× 30 359
Mexhid Ferati Sweden 10 24 0.4× 20 0.3× 45 1.3× 14 0.5× 17 0.7× 27 224

Countries citing papers authored by Michael Liut

Since Specialization
Citations

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

Fields of papers citing papers by Michael Liut

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Liut

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Liut. A scholar is included among the top collaborators of Michael Liut 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 Michael Liut. Michael Liut 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
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Kolog, Emmanuel Awuni, Oscar Karnalim, Jack Parkinson, et al.. (2025). The Impostor Phenomenon in the Global Computing Graduate Student Population. VTechWorks (Virginia Tech). 322–331.
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Martín-Barreiro, Carlos, et al.. (2025). Improving SVM performance through data reduction and misclassification analysis with linear programming. Complex & Intelligent Systems. 11(8). 356–356. 1 indexed citations
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Taipalus, Toni, et al.. (2025). Data Systems Education: Curriculum Recommendations, Course Syllabi, and Industry Needs. Open Access Institutional Repository at Robert Gordon University (Robert Gordon University). 95–123.
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Williams, Joseph Jay, et al.. (2024). Student Interaction with Instructor Emails in Introductory and Upper-Year Computing Courses. 1477–1483. 1 indexed citations
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Karnalim, Oscar, et al.. (2024). Detecting LLM-Generated Text in Computing Education: Comparative Study for ChatGPT Cases. 121–126. 30 indexed citations
10.
Bhattacharjee, Ananya, Rachel Kornfield, Syed Ishtiaque Ahmed, et al.. (2024). Understanding the Role of Large Language Models in Personalizing and Scaffolding Strategies to Combat Academic Procrastination. PubMed. 2024. 1–18. 9 indexed citations
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Taipalus, Toni, et al.. (2024). Curriculum Analysis for Data Systems Education. Open Access Institutional Repository at Robert Gordon University (Robert Gordon University). 761–762.
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Williams, Joseph Jay, et al.. (2024). Guiding Students in Using LLMs in Supported Learning Environments: Effects on Interaction Dynamics, Learner Performance, Confidence, and Trust. Proceedings of the ACM on Human-Computer Interaction. 8(CSCW2). 1–30. 1 indexed citations
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Williams, Joseph Jay, et al.. (2024). Exploring Self-Explanations in a Flipped Database Course. 20–26. 1 indexed citations
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Liut, Michael, et al.. (2023). MSMI1: Towards a Validated SQL Misconceptions Instrument. TU/e Research Portal. 16–17. 1 indexed citations
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Liut, Michael, et al.. (2022). The Positive Effects of using Reflective Prompts in a Database Course. 32–37. 6 indexed citations
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Ly, Anna, John Edwards, Michael Liut, & Andrew Petersen. (2021). Revisiting Syntax Exercises in CS1. 9–14. 4 indexed citations
20.
Franěk, František & Michael Liut. (2019). Algorithms to Compute the Lyndon Array Revisited.. 16–28.

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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