John M. Aiken

457 total citations
22 papers, 264 citations indexed

About

John M. Aiken is a scholar working on Geophysics, Computer Science Applications and Mechanics of Materials. According to data from OpenAlex, John M. Aiken has authored 22 papers receiving a total of 264 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Geophysics, 6 papers in Computer Science Applications and 5 papers in Mechanics of Materials. Recurrent topics in John M. Aiken's work include Experimental Learning in Engineering (5 papers), Online Learning and Analytics (4 papers) and Innovative Teaching Methods (4 papers). John M. Aiken is often cited by papers focused on Experimental Learning in Engineering (5 papers), Online Learning and Analytics (4 papers) and Innovative Teaching Methods (4 papers). John M. Aiken collaborates with scholars based in United States, Norway and France. John M. Aiken's co-authors include François Renard, Jessica McBeck, Marcos D. Caballero, Yehuda Ben‐Zion, Michael F. Schatz, Charles L. Salzberg, H. J. Lewandowski, B. Cordonnier, Brian D. Thoms and Riccardo De Bin and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

John M. Aiken

20 papers receiving 247 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John M. Aiken United States 11 68 59 48 47 41 22 264
John W. McCormick United States 8 19 0.3× 71 1.2× 45 0.9× 29 0.6× 31 0.8× 26 277
Heng Luo China 8 54 0.8× 31 0.5× 38 0.8× 39 0.8× 23 0.6× 17 472
Mary J. S. Roth United States 13 71 1.0× 279 4.7× 26 0.5× 20 0.4× 24 0.6× 46 632
Barbara J. Tewksbury United States 10 213 3.1× 64 1.1× 17 0.4× 12 0.3× 19 0.5× 31 403
Matthew d'Alessio United States 11 57 0.8× 578 9.8× 55 1.1× 9 0.2× 47 1.1× 23 706
Cinzia Cervato United States 10 151 2.2× 24 0.4× 11 0.2× 12 0.3× 32 0.8× 45 375
Lisa A. Gilbert United States 11 60 0.9× 231 3.9× 24 0.5× 7 0.1× 49 1.2× 27 406
Michael Rogers United States 10 66 1.0× 26 0.4× 7 0.1× 12 0.3× 5 0.1× 34 211
Edward B. Nuhfer United States 9 110 1.6× 13 0.2× 9 0.2× 8 0.2× 10 0.2× 27 291

Countries citing papers authored by John M. Aiken

Since Specialization
Citations

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

Fields of papers citing papers by John M. Aiken

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John M. Aiken. 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 John M. Aiken. The network helps show where John M. Aiken may publish in the future.

Co-authorship network of co-authors of John M. Aiken

This figure shows the co-authorship network connecting the top 25 collaborators of John M. Aiken. A scholar is included among the top collaborators of John M. Aiken 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 John M. Aiken. John M. Aiken 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.
Aiken, John M., et al.. (2025). An AI‐Enabled Data Processing Pipeline for Ingesting Borehole Data in Peridotite Environments. SHILAP Revista de lepidopterología. 2(2).
2.
Aiken, John M., et al.. (2024). A Bayesian spatio-temporal model of COVID-19 spread in England. Scientific Reports. 14(1). 10335–10335. 1 indexed citations
3.
Pollard, Benjamin, et al.. (2022). Classification of open-ended responses to a research-based assessment using natural language processing. Physical Review Physics Education Research. 18(1). 24 indexed citations
4.
Aiken, John M., et al.. (2022). A Machine Learning Framework to Automate the Classification of Surge‐Type Glaciers in Svalbard. Journal of Geophysical Research Earth Surface. 127(7). 9 indexed citations
5.
McBeck, Jessica, John M. Aiken, B. Cordonnier, Yehuda Ben‐Zion, & François Renard. (2021). Predicting Fracture Network Development in Crystalline Rocks. Pure and Applied Geophysics. 179(1). 275–299. 10 indexed citations
6.
Aiken, John M. & H. J. Lewandowski. (2021). Data sharing model for physics education research using the 70 000 response Colorado Learning Attitudes about Science Survey for Experimental Physics dataset. Physical Review Physics Education Research. 17(2). 4 indexed citations
7.
Aiken, John M., Riccardo De Bin, M. Hjorth‐Jensen, & Marcos D. Caballero. (2020). Predicting time to graduation at a large enrollment American university. PLoS ONE. 15(11). e0242334–e0242334. 22 indexed citations
8.
McBeck, Jessica, John M. Aiken, Joachim Mathiesen, Yehuda Ben‐Zion, & François Renard. (2020). Deformation Precursors to Catastrophic Failure in Rocks. Geophysical Research Letters. 47(24). 24 indexed citations
9.
McBeck, Jessica, John M. Aiken, Yehuda Ben‐Zion, & François Renard. (2020). Predicting the proximity to macroscopic failure using local strain populations from dynamic in situ X-ray tomography triaxial compression experiments on rocks. Earth and Planetary Science Letters. 543. 116344–116344. 26 indexed citations
10.
McBeck, Jessica, Neelima Kandula, John M. Aiken, B. Cordonnier, & François Renard. (2019). Isolating the Factors That Govern Fracture Development in Rocks Throughout Dynamic In Situ X‐Ray Tomography Experiments. Geophysical Research Letters. 46(20). 11127–11135. 19 indexed citations
11.
Allen, Grant, et al.. (2019). Identifying features predictive of faculty integrating computation into physics courses. Physical Review Physics Education Research. 15(1). 14 indexed citations
12.
Aiken, John M., Chastity Aiken, & Fabrice Cotton. (2018). A Python Library for Teaching Computation to Seismology Students. Seismological Research Letters. 89(3). 1165–1171. 2 indexed citations
13.
Aiken, John M., et al.. (2017). Exploring physics students’ engagement with online instructional videos in an introductory mechanics course. Physical Review Physics Education Research. 13(2). 20138–20138. 25 indexed citations
14.
Aiken, John M., et al.. (2017). Peer assessment of student-produced mechanics lab report videos. Physical Review Physics Education Research. 13(2). 2 indexed citations
15.
Aiken, John M., et al.. (2016). Do-It-Yourself Whiteboard-Style Physics Video Lectures. The Physics Teacher. 55(1). 22–24. 9 indexed citations
16.
Lin, Shih‐Yin, John M. Aiken, Chien‐Lin Liu, et al.. (2015). Peer Evaluation of Video Lab Reports in an Introductory Physics MOOC. The Physics Video Demonstration Database (Cornell University). 163–166. 3 indexed citations
17.
Caballero, Marcos D., et al.. (2013). Integrating Numerical Computation into the Modeling Instruction Curriculum. The Physics Teacher. 52(1). 38–42. 17 indexed citations
18.
Aiken, John M., et al.. (2006). Using zooplankton biomass size spectra to assess ecological change in a well-studied freshwater lake ecosystem: Oneida Lake, New York. Canadian Journal of Fisheries and Aquatic Sciences. 63(12). 2687–2699. 16 indexed citations
19.
Aiken, John M., et al.. (1981). Treatment or Involuntary Euthanasia for Severely Handicapped Newborns: Issues of Philosophy and Public Policy. Research and Practice for Persons with Severe Disabilities. 6(4). 3–10. 1 indexed citations
20.
Webber, Milo M., et al.. (1973). Telecommunication of Images in the Practice of Diagnostic Radiology. Radiology. 109(1). 71–74. 16 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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