John Aiken

Researcher - NJORD Centre for Studies of the Physics of the Earth

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Email john.aiken@fys.uio.no

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Visiting address Sem Sælands vei 24 Fysikkbygningen 0371 Oslo

Postal address Postboks 1048 0371 Oslo

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Academic interests

After defending my PhD in 2020, I currently work as a researcher in the Njord Centre. I am a data scientist whose research background has focused on integrating machine learning within the problem spaces of seismology, rock physics, glaciology, and previously educational data mining. This has included investigating rock failure in triaxial compression experiments, classifying glacier instabilities, building image segmentation models to understand X-ray computed tomography data, and exploring seismic wave form data from the Oman Multi-Borehole Observatory. In my previous scientific life I also investigated broadly what leads students to drop out of university. You can find my full publication list on my google scholar profile.

Aiken, John & Lewandowski, H.J. (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. ISSN 2469-9896. 17(2). doi: 10.1103/PhysRevPhysEducRes.17.020144. Full text in Research Archive
McBeck, Jessica Ann; Aiken, John; Cordonnier, B.; Ben-Zion, Yehuda & Renard, Francois (2021). Predicting Fracture Network Development in Crystalline Rocks. Pure and Applied Geophysics (PAGEOPH). ISSN 0033-4553. 179, p. 275–299. doi: 10.1007/s00024-021-02908-7. Full text in Research Archive
Aiken, John Mark; De Bin, Riccardo; Lewandowski, Heather & Caballero, Marcos Daniel (2021). Framework for evaluating statistical models in physics education research. Physical Review Physics Education Research. ISSN 2469-9896. 17(2). doi: 10.1103/PhysRevPhysEducRes.17.020104. Full text in Research Archive
McBeck, Jessica Ann; Aiken, John Mark; Ben-Zion, Yehuda & Renard, Francois (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. ISSN 0012-821X. 543. doi: 10.1016/j.epsl.2020.116344. Full text in Research Archive
McBeck, Jessica Ann; Aiken, John Mark; Mathiesen, Joachim; Ben-Zion, Yehuda & Renard, Francois (2020). Deformation precursors to catastrophic failure in rocks. Geophysical Research Letters. ISSN 0094-8276. 47(24). doi: 10.1029/2020GL090255. Full text in Research Archive
Aiken, John Mark; De Bin, Riccardo; Hjorth-Jensen, Morten & Caballero, Marcos Daniel (2020). Predicting time to graduation at a large enrollment American university. PLOS ONE. ISSN 1932-6203. doi: 10.1371/journal.pone.0242334. Full text in Research Archive
McBeck, Jessica Ann; Kandula, Neelima; Aiken, John Mark; Cordonnier, Benoit & Renard, Francois (2019). Isolating the factors that govern fracture development in rocks throughout dynamic in situ X-ray tomography experiments. Geophysical Research Letters. ISSN 0094-8276. 46(20), p. 11127–11135. doi: 10.1029/2019GL084613. Full text in Research Archive
Knaub, Alexis V; Aiken, John Mark & Ding, Lin (2019). Two-phase study examining perspectives and use of quantitative methods in physics education research. Physical Review Physics Education Research. ISSN 2469-9896. 15(2). doi: 10.1103/PhysRevPhysEducRes.15.020102. Full text in Research Archive
Aiken, John Mark; Henderson, Rachel & Caballero, Marcos Daniel (2019). Modeling student pathways in a physics bachelor's degree program. Physical Review Physics Education Research. ISSN 2469-9896. 15(1), p. 1–17. doi: 10.1103/PhysRevPhysEducRes.15.010128. Full text in Research Archive
Young, Nicholas; Allen, Grant; Aiken, John Mark; Henderson, Rachel & Caballero, Marcos Daniel (2019). Identifying features predictive of faculty integrating computation into physics courses. Physical Review Physics Education Research. ISSN 2469-9896. 15(1). doi: 10.1103/PhysRevPhysEducRes.15.010114. Full text in Research Archive
Aiken, John Mark; Aiken, Chastity & Cotton, Fabrice (2018). A python library for teaching computation to seismology students. Seismological Research Letters. ISSN 0895-0695. 89(3), p. 1165–1171. doi: 10.1785/0220170246.
Lin, Shih-Yin; Aiken, John Mark; Seaton, Daniel T; Douglas, Scott; Greco, Edwin F & Thoms, Brian D [Show all 7 contributors for this article] (2017). Exploring physics students' engagement with online instructional videos in an introductory mechanics course. Physical Review Physics Education Research. ISSN 2469-9896. 13(2). doi: 10.1103/PhysRevPhysEducRes.13.020138.

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Knaub, Alexis V.; Aiken, John & Caballero, Marcos (2019). Editorial: Focused Collection: Quantitative Methods in PER: A Critical Examination. Physical Review Physics Education Research. ISSN 2469-9896. 15(2). doi: 10.1103/PhysRevPhysEducRes.15.020001.
Aiken, John Mark (2020). Understanding University Student Pathways Towards Graduation with Machine Learning and Institutional Data. Det matematisk naturvitenskapelige fakultet.

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Published Nov. 6, 2017 11:32 AM - Last modified Jan. 24, 2022 4:30 PM