Mark J. Gierl

5.4k total citations
128 papers, 3.5k citations indexed

About

Mark J. Gierl is a scholar working on Artificial Intelligence, Management Science and Operations Research and Education. According to data from OpenAlex, Mark J. Gierl has authored 128 papers receiving a total of 3.5k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 42 papers in Management Science and Operations Research and 31 papers in Education. Recurrent topics in Mark J. Gierl's work include Psychometric Methodologies and Testing (41 papers), Intelligent Tutoring Systems and Adaptive Learning (24 papers) and Educational Technology and Assessment (21 papers). Mark J. Gierl is often cited by papers focused on Psychometric Methodologies and Testing (41 papers), Intelligent Tutoring Systems and Adaptive Learning (24 papers) and Educational Technology and Assessment (21 papers). Mark J. Gierl collaborates with scholars based in Canada, United States and Türkiye. Mark J. Gierl's co-authors include Jacqueline P. Leighton, Michael G. Jodoin, Hollis Lai, S. Hunka, Jeffrey Bisanz, Ying Cui, Qi Guo, Terry A. Ackerman, Okan Bulut and Cindy M. Walker and has published in prestigious journals such as SHILAP Revista de lepidopterología, Review of Educational Research and Frontiers in Psychology.

In The Last Decade

Mark J. Gierl

122 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark J. Gierl Canada 33 1.2k 1.1k 877 727 506 128 3.5k
Mark D. Reckase United States 28 1.7k 1.5× 1.1k 1.0× 334 0.4× 552 0.8× 463 0.9× 130 4.2k
Jimmy de la Torre United States 33 2.1k 1.8× 695 0.7× 1.6k 1.8× 637 0.9× 830 1.6× 109 4.6k
Kikumi K. Tatsuoka United States 26 1.2k 1.0× 717 0.7× 1.1k 1.2× 675 0.9× 393 0.8× 76 3.4k
Neil J. Dorans United States 30 2.4k 2.1× 960 0.9× 359 0.4× 657 0.9× 698 1.4× 157 4.8k
Rob R. Meijer Netherlands 40 1.5k 1.3× 522 0.5× 379 0.4× 632 0.9× 965 1.9× 162 5.4k
Matthias von Davier United States 32 1.7k 1.5× 976 0.9× 580 0.7× 443 0.6× 650 1.3× 135 3.8k
Jonathan Templin United States 23 1.2k 1.1× 419 0.4× 959 1.1× 435 0.6× 493 1.0× 55 3.0k
Allan S. Cohen United States 34 1.7k 1.5× 761 0.7× 304 0.3× 460 0.6× 445 0.9× 130 3.5k
Hariharan Swaminathan United States 22 2.4k 2.1× 1.0k 1.0× 299 0.3× 963 1.3× 638 1.3× 42 5.0k
Michael T. Kane United States 33 1.3k 1.1× 2.3k 2.2× 271 0.3× 937 1.3× 264 0.5× 113 5.5k

Countries citing papers authored by Mark J. Gierl

Since Specialization
Citations

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

Fields of papers citing papers by Mark J. Gierl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark J. Gierl. 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 Mark J. Gierl. The network helps show where Mark J. Gierl may publish in the future.

Co-authorship network of co-authors of Mark J. Gierl

This figure shows the co-authorship network connecting the top 25 collaborators of Mark J. Gierl. A scholar is included among the top collaborators of Mark J. Gierl 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 Mark J. Gierl. Mark J. Gierl 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.
Sonnleitner, Philipp, et al.. (2025). Establishing Cognitive Item Models for Fair and Theory-Grounded Automatic Item Generation: A Large-Scale Assessment Study with Image-Based Math Items. Applied Measurement in Education. 38(2). 95–117. 1 indexed citations
2.
Shin, Jinnie, et al.. (2024). An engagement-aware predictive model to evaluate problem-solving performance from the study of adult skills' (PIAAC 2012) process data. Large-scale Assessments in Education. 12(1). 2 indexed citations
3.
4.
Gierl, Mark J., et al.. (2024). Using OpenAI GPT to Generate Reading Comprehension Items. Educational Measurement Issues and Practice. 43(1). 5–18. 10 indexed citations
5.
Gibson, Monica Prasad, et al.. (2024). Automating bone loss measurement on periapical radiographs for predicting the periodontitis stage and grade. SHILAP Revista de lepidopterología. 5. 1479380–1479380.
6.
Gierl, Mark J., et al.. (2024). Using Automated Procedures to Score Educational Essays Written in Three Languages. Journal of Educational Measurement. 62(1). 33–56. 3 indexed citations
7.
Gierl, Mark J., et al.. (2023). Automatic item generation for online measurement and evaluation: Turkish literature items. SHILAP Revista de lepidopterología. 10(2). 218–231.
8.
Latifi, Syed & Mark J. Gierl. (2020). Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing. Language Testing. 38(1). 62–85. 33 indexed citations
9.
Gierl, Mark J., Lia M. Daniels, & Xinxin Zhang. (2017). Creating Parallel Forms to Support On-Demand Testing for Undergraduate Students in Psychology. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi. 288–302. 5 indexed citations
10.
Lai, Hollis, et al.. (2016). Using Automatic Item Generation to Improve the Quality of MCQ Distractors. Teaching and Learning in Medicine. 28(2). 166–173. 31 indexed citations
11.
Lai, Hollis, Mark J. Gierl, & Оксана Бабенко. (2015). Application of Conditional Means for Diagnostic Scoring. International Journal of Learning Teaching and Educational Research. 12(3). 6 indexed citations
12.
Gierl, Mark J., et al.. (2009). Validating Cognitive Models of Task Performance in Algebra on the SAT®. Research Report No. 2009-3.. 2 indexed citations
13.
Gierl, Mark J., et al.. (2008). Using Connectionist Models to Evaluate Examinees’ Response Patterns to Achievement Tests. Journal of Modern Applied Statistical Methods. 7(1). 234–245. 18 indexed citations
14.
Leighton, Jacqueline P. & Mark J. Gierl. (2007). Cognitive Diagnostic Assessment for Education. Cambridge University Press eBooks. 299 indexed citations
15.
Cheng, Irene, Mark J. Gierl, & Anup Basu. (2006). Evaluating Performance Features with 3D Item Types for use with Computer-Based Tests in Education.. 208–214. 2 indexed citations
16.
Gierl, Mark J., Xuan Tan, & Changjiang Wang. (2005). Identifying Content and Cognitive Dimensions on the SAT®. Research Report No. 2005-11.. 4 indexed citations
17.
Rogers, W. Todd, et al.. (2003). Differential Validity and Utility of Successive and Simultaneous Approaches to the Development of Equivalent Achievement Tests in French and English. Alberta Journal of Educational Research. 49(3). 7 indexed citations
18.
Gierl, Mark J.. (1999). Differential Item Functioning on the Alberta Education Social Studies 30 Diploma Examination.. 33(2). 54–58. 3 indexed citations
19.
Gierl, Mark J., W. Todd Rogers, & Don A. Klinger. (1999). Using Statistical and Judgmental Reviews to Identify and Interpret Translation Differential Item Functioning. Alberta Journal of Educational Research. 45(4). 21 indexed citations
20.
Gierl, Mark J.. (1998). Generalizability of Written-Response Scores for the Alberta Education English 30 Diploma Examination.. Alberta Journal of Educational Research. 44(1). 94–97. 2 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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