Education Policy Lab Presentation: May 21, 2018 - "Instructional Techniques in Large, Undergraduate STEM Lecture Courses: Exploring Patterns Using MCA"

Gabe Orona will be presenting Monday, May 21 at the Education Policy Lab Meeting, 12:00-1:00 pm, in Education 2010: "Instructional Techniques in Large, Undergraduate STEM Lecture Courses: Exploring Patterns Using MCA."

Abstract

Jobs in STEM (science, technology, engineering, and mathematics) demand proficient and highly skilled workers, and university education is seen by policymakers as the engine of supply. As such, undergraduate STEM education has become of primary national interest, especially as many of these courses have traditionally taken place in large lecture-based classes that usually don’t allow students more personalized time with their instructors. Recent research has focused on enhancing STEM education to bolster the number of university graduates in these areas and has promoted the adoption of a body of instructional techniques (coined “promising practices”) aimed to increase the quality of teaching and learning in STEM (National Research Council, 2012).

In this study, we extend an underutilized data-reduction technique, multiple correspondence analysis (MCA), to classroom observations for the purpose of exploring patterns among STEM teaching practices across instructor characteristics. This method maintains the unique advantage over other approaches not only by uncovering groupings of promising practices, but also in its ability to examine associations with variables not included (supplementary) in the computation of dimensions. Our results reveal how this method proved useful for describing patterns of teaching practices. The substantive findings from this descriptive analysis reveal that the implementation of promising practices varies according to instructor rank classification, gender, and school. Implications for the hiring of faculty dedicated to teaching are also considered, as our results speak to how job incentive structures may be important to the implementation of these instructional methods. Future research should consider testing the links between instructor characteristics, teaching practice dimensions, and student outcomes for a mechanistic account that ties these results with studies showing the effects of promising practices on student outcomes.