Associate Dean for Undergraduate Education, College of Literature, Science and the Arts, and Arthur F. Thurnau Professor of Physics, Astronomy, and Education
I am a data scientist. My research teams use the sensibilities of scientists to explore and draw inferences about the world from large data sets of many kinds.
We have a long heritage of work in astrophysics. For the last 20 years, we have used survey data to publish papers on many topics: ultra-high energy cosmic rays, variable stars of many kinds, galaxy masses and morphologies, galaxy filaments, groups, and clusters, quasars, meteors, gravitational lensing, gamma-ray bursts, x-ray astronomy, and cosmology. Our main data sources have been the Sloan Digital Sky Survey, the Robotic Optical Transient Search Experiment, and the Dark Energy Survey.
In recent years we have branched out, turning most of our attention to Learning Analytics: using data to understand and improve teaching and learning. We are exploring grading patterns and performance disparities both at Michigan and across the CIC, developing a variety of data driven student support tools like E2Coach through the Digital Innovation Greenhouse, an innovation space for exploring the personalization of education, and launching the NSF funded REBUILD project. REBUILD is an interdisciplinary collaboration, fostering the creation of intergenerational research teams including undergrads, grad students, postdocs, and faculty who will apply a scientific, evidence-based approach to teaching and learning in physics, chemistry, astronomy, biology and math.
We are always seeking new collaborators and encourage any undergraduates, graduate students, postdocs, or faculty members whose interests overlap with ours to get in touch.
Koester, B., Grom, G., & McKay, T., 2016, “Patterns of Gendered Performance Differences in Introductory STEM Courses”, submitted to PLoS One.
Michelotti, N., Tritz, J., Winn, D., & McKay, T., 2015, “Tournament-Model for Peer Evaluation in an Upper Level Physics Course”, submitted to The American Journal of Physics.
Zhang, Y., et al., 2015 “Galaxies in X-Ray Selected Clusters and Groups in Dark Energy Survey Data I: Stellar Mass Growth of Bright Central Galaxies Since Z~1.2”, accepted for publication in The Astrophysical Journal, also arXiv:1504.02983.
Zhang, Y., McKay, T., Bertin, E., Jeltema, T., Miller, C., Rykoff, E., and Song, J., 2015, “Crowded Cluster Cores: Algorithms for Deblending in Dark Energy Survey Images”, Proceedings of the Astronomical Society of the Pacific, 127, 1183.
Barthelemy, R., Hedberg, G., Greenberg, A., & McKay, T., 2015, “The Climate Experiences of Students in Introductory Biology”, Journal of Microbiology and Biology Education, 16.2.
Huberth, M., Chen, P., Tritz, J., & McKay, T. A., 2015, “Computer-Tailored Student Support in Introductory Physics”, PloS ONE, 10(9), e0137001.
Dietrich, J., et al., 2014, “Orientation Bias of Optically Selected Galaxy Clusters and its Impact on Stacked Weak Lensing Analyses”, Monthly Notices of the Royal Astronomical Society, 443, 1713.
Huberth, M., Michelotti, N., and McKay, T., 2014, “E2Coach: Tailoring Support for Students in Introductory STEM Courses”, EDUCAUSE Review, 48, 6.
Cui , X., et al., 2014, “The Optical Luminosity Function Of Gamma-Ray Bursts Deduced From ROTSE Observations”, The Astrophysical Journal, 795, 103.
Evrard, A., Miller, M., Winn, D., Jones, K., Tritz, J., and McKay, T., 2015, “Problem Roulette: Studying Introductory Physics in the Cloud”, The American Journal of Physics, 83, 76.
Wright, M., McKay, T., Hershock, C., Miller, K., and Tritz, J., 2014, “Better-Than-Expected: Learning from Students to Promote Success in Introductory Physics Courses”, Change: The Magazine of Higher Learning, 46:1, 28.
Field(s) of Study
- Data Science
- Learning Analytics
- Physics and Astronomy Education
- Observational Cosmology
- Galaxy Clusters