National Geoscience Faculty Survey 2016: Prevalence of systems thinking and scientific modeling learning opportunities. Issue 2 (3rd April 2019) - Lally, Diane; Forbes, Cory T.; McNeal, Karen S.; Soltis, Nicholas A.
Abstract: Scientific modeling and systems thinking (SMST) is central to the geosciences, yet few studies have documented how and to what extent undergraduate geoscience courses emphasize SMST, as well as factors that might help explain or predict these trends. Here, we present research based on data ( n = 2056) from the most recent (2016) administration of the National Geoscience Faculty Survey, administered to a national sample of postsecondary geoscience instructors in the United States. We investigated instructor- and course-related variables as they relate to a set of 9 survey items that serve as a composite measure for SMST. Significant variation was observed in reported frequencies of individual SMST practices in undergraduate geoscience courses. The highest levels of reported SMST were associated with faculty from atmospheric and environmental sciences, those who emphasized research-based, student-centered pedagogical practices, those who recently made changes to both course content and teaching methods, and those who reported high levels of engagement in instructional improvement activities (workshops, presentations, seminars). Reported SMST practices were similar for faculty identifying as geoscientists and geoscience education researchers, and both were significantly higher than for teaching-focused faculty who do not conduct research. A linear regression model including variables found to be significant in the analyses was able to predict 17% of the overall variance in reported SMST practices. These findings illustrate the importance of instructors' disciplinary orientation and active engagement in instructional innovation as related to SMST, and provide important points of impact for enhancing SMST in undergraduate geoscience courses through course design and faculty development. However, the relatively modest predictive power of the regression model indicates there are many other factors influencing SMST that warrant future research.