BPS Room 1310A
567 Wilson Road
East Lansing, MI 48824
I work in the Department of Physics and Astronomy and the Department of Computational Mathematics, Science and Engineering. I hold the Lappan-Phillips Chair of Physics Education, co-direct the Physics Education Research Lab, serve as a principal investigator for the Learning Machines Lab, conduct research as part of the newly-founded Computational Education Research Lab, and hold an appointment as research faculty at the University of Oslo’s Center for Computing in Science Education. I study how tools and science practices affect student learning in physics and computational science, and the conditions and environments that support or inhibit that learning.
I earned my B.S. in physics from the University of Texas at Austin in 2004. I worked on opto-microfluidics transport and control experiments at the Georgia Institute of Technology earning my M.S. in physics before shifting my research focus to physics education. I helped found the Georgia Tech Physics Education Research group in 2007 and earned the first physics education focused Ph.D. from Georgia Tech in 2011 conducting research in computing-integrated introductory physics courses. I moved to the University of Colorado Boulder as a postdoctoral researcher and helped transform upper-division physics courses to adopt more active learning and evidence-based approaches.
My work spans from the high school to the upper-division. I am particularly interested in how students learn through their use of tools such as mathematics and computing. My work employs cognitive and sociocultural theories of learning and aims to blend these perspectives to enhance physics and computational science instruction at all levels. My projects range from the fine-grained (e.g., how students engage with particular computing ideas) to the course-scale (e.g., what kind of things that students are able to do after instruction in computing) to the very broad (e.g., how do departments value computing and work to integrate it). My work includes the use of data science to address questions in STEM education.