Wisdome: Quantitative Reasoning and Mathematical Modeling Working Group

The Quantitative Reasoning and Mathematical Modeling (QRaMM) working group is one of three research strands initiated by the Wyoming Institute for the Study and Development of Mathematical Education (WISDOMe). QRaMM brings together researchers from multiple universities across the country to share ideas and conduct research on quantitative reasoning and modeling within an interdisciplinary context. As part of the work of QRaMM and in collaboration with the National Science Foundation MSP Pathways in Environmental Literacy Project, a national virtual seminar QRaMM in Science was initiated in Spring 2011. The seminar engaged 15 speakers from across the country in the online video conference, including experts in quantitative reasoning and modeling from the fields of science, computational science, mathematics, science education, and mathematics education. The context for the discussion of quantitative reasoning and modeling was environmental literacy. The issues in psychology of mathematics education for the seminar included students’ development of quantitative reasoning and mathematical modeling from 6th to 12th grade, creation of parallel QRaMM learning progressions for those being created by the MSP Pathways project for environmental literacy, and study of the impact and interplay of QR and modeling on students’ development of environmental literacy. The national experts informed a QRaMM Science research team consisting of faculty and graduate students who developed Environmental Science QR interview protocols based on MSP Pathways learning progressions. Data from these protocols will lead to the creation of Environmental Science QR assessments and research-based professional development that supports learning environments that lead to environmentally literate citizens capable of making informed decisions about the grand challenges facing the next generation. Participants in the QRaMM working group will join members of the QRaMM research team in critiquing environmental science QRaMM learning progressions, interpret data on QR in the sciences, vet QRaMM assessment items, and debate the QRaMM theoretical framework.