Quantitative Modeling in Biology Undergrad (QM BUG) Courses: Teaching Approaches and Student Outcome

Joe Dauer
Associate Professor of Life Science Education
University of Nebraska-Lincoln

For some time now, national calls have emphasized inclusion of quantitative reasoning (like data and figure interpretation and mathematical and computational modeling) in teaching STEM disciplines. Some in the biology community have responded through increased quantitative integration in their biology teaching. Our project aims to characterize the learning environment when instructors incorporate quantitative reasoning in undergraduate biology instruction. We hypothesized that instructors create learning environments that are conducive to different types of quantitative reasoning including classrooms that emphasize quantitative modeling (QM) and quantitative interpretation (QI). QM includes model-based reasoning, creating and refining models, and using variety of methods to construct model whereas QI implies one’s ability to discover trends, make prediction, translate models. We are developing learning environment profiles based on qualitative and quantitative data about instructor intentions, instructor implementation, and student abilities in quantitative reasoning skills. Fifteen instructors teaching life science courses at institutions across the US have submitted videos of themselves teaching quantitative reasoning in biology. The video recordings will be analyzed using the Quantitative Modeling Observation Protocol (QMOP), an interdisciplinary teaching observation protocol currently being validated. Student abilities will be categorized using an established assessment of quantitative modeling skills. A semi-structured interview will clarify instructors’ pedagogical content knowledge at the interface of math and biology. Collectively, the observed teaching, the instructor intentions, and the student ability data will facilitate triangulation on learning environment profiles. Preliminary work has led to two broad profiles, learning environments that emphasize quantitative interpretation of figures and data tables and learning environments that emphasize model creation, revision, and application. For instructors seeking to include quantitative reasoning into biology classrooms, this work will highlight pedagogy that creates innovative, quantitative reasoning-focused learning environments.


Brian Couch, University of Nebraska-Lincoln