g:i ag:i aAssessing A Student Matching Tool - AIM Analyticsg:i a - g:i a


Event Details

Becky Matz and Ezra Brooks will present on assessing the long-term impacts of three-dimensional learning using a student matching tool.

Researchers and educators at Michigan State University are currently engaged in three projects that share a common objective of transforming gateway biology, chemistry, and physics courses so that they focus on three-dimensional (3D) learning, a construct defined by the NRC Framework for K-12 Science Education. The three dimensions are scientific practices (what students should do with their knowledge), crosscutting concepts (themes across science disciplines), and core ideas (the explanatory and generative ideas that students really need to know). Each of the three projects focuses on bringing 3D learning into the gateway curriculum in a different way; we are studying the summative impact of a gateway curriculum that focuses on scientific practices, crosscutting concepts, and core ideas. Here, we focus on assessing the impact of 3D learning on student persistence in STEM through the use of a student matching tool. Although regression analyses are typically used, the method of exactly matching students according to their recorded characteristics is a more robust method for accounting for self-selection bias in quasi-experimental education studies. This tool handles the features and idiosyncrasies of MSU student data across multiple institutional databases.


Becky Matz is an Assistant Professor in the CREATE for STEM Institute. Along with Sarah Jardeleza, she is participating in the CNS Biology Initiative and conducting research to evaluate the outcomes of these efforts. Becky brings experience in both disciplinary-based science research (Chemistry and Cell Biology) and Science Education research. Becky earned a Ph.D. in Chemistry and an M.S. in Educational Studies from University of Michigan, and was a postdoc at Michigan’s Center for Research on Learning and Teaching.

Ezra Brooks is a programmer at Michigan State University specializing in R/Shiny apps for statistical analysis, Data Visualization (D3, Bokeh), Machine Learning, Python, C++, RHEL, OS XMySQL, and PostgreSQL.


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