The School of Natural Resources is hosting a spring seminar series for graduate students to share their research.
Pizza will be provided. All are welcome to join!

Anum Khushal - Characterizing Learning Environment for Quantitative Reasoning Skills in Undergraduate Biology

National calls have emphasized the inclusion of quantitative reasoning (QR) such as data and figure interpretation and mathematical and computational modeling in teaching STEM discipline. We aim to characterize the learning environments whereby instructors incorporate QR in undergraduate biology instruction based on qualitative and quantitative data about instructor intentions, QR implementation and student performance in QR skills. We hypothesize that instructors create learning environments that are conducive to different QR types including quantitative interpretations (predicting, translating models), quantitative modeling (mathematical conceptualization, creating, refining models) and meta modeling (model-based reasoning). Eighteen instructors teaching life science courses at US institutions submitted videos of themselves teaching QR in biology. The video recordings were analyzed using the Quantitative Modeling Observation Protocol a validated interdisciplinary teaching observation protocol. Student performance was measured using an assessment of quantitative modeling skills. A semi-structured interview was conducted with each instructor to explore their pedagogical content knowledge at the interface of math and biology and the instructor intentional integration of quantitative reasoning in life science courses. Collectively, the observed teaching, instructor intentions, and student performance data will facilitate triangulation on learning environments created to include QR in classroom. Preliminary work has led to two broad profiles, learning environments that emphasize quantitative interpretation of figures and data tables and learning environments that additionally emphasize model creation, revision, and application. For instructors seeking to include QR into biology or other STEM discipline classroom this work will highlight pedagogy that creates innovative, QR-focused learning environments.