Search for: All records

A cycle of prediction, comparison, and feedback supports spatial learning in geoscience. 

Previous research demonstrates that domain experts, like ordinary participant populations, are vulnerable to decision bias. Here, we examine susceptibility to bias amongst expert field scientists. Field scientists operate in less predictable environments than other experts, and feedback on the consequences of their decisions is often unclear or delayed. Thus, field scientists are a population where the findings of scientific research may be particularly vulnerable to bias. In this study, susceptibility to optimism, hindsight, and framing bias was evaluated in a group of expert field geologists using descriptive decision scenarios. Experts showed susceptibility to all three biases, and susceptibility was not influenced by years of science practice. We found no evidence that participants' vulnerability to one bias was related to their vulnerability to another bias. Our findings are broadly consistent with previous research on expertise and decision bias, demonstrating that no expert, regardless their domain experience, is immune to bias.

 

There is a large gap between the ability of experts and students in grasping spatial concepts and representations. Engineering and the geosciences require the highest expertise in spatial thinking, and weak spatial skills are a significant barrier to success for many students [1]. Spatial skills are also highly malleable [2]; therefore, a current challenge is to identify how to promote students’ spatial thinking. Interdisciplinary research on how students think about spatially-demanding problems in the geosciences has identified several major barriers for students and interventions to help scaffold learning at a variety of levels from high school through upper level undergraduate majors. The Geoscience Education Transdisciplinary Spatial Learning Network (GET-Spatial; http://serc.carleton.edu/getspatial/) is an NSF-funded collaboration between geoscientists, cognitive psychologists, and education researchers. Our goal is to help students overcome initial hurdles in reasoning about spatial problems in an effort to diversify the geoscience workforce. Examples of spatial problems in the fields of geochemistry include scaling, both in size and time; penetrative thinking to make inferences about internal structures from surface properties; and graph-reading, especially ternary diagrams. Understanding scales outside of direct human experience, both very large (e.g. cosmochemistry, deep time) and very small (e.g. mineralogy, nanoparticles) can be acutely difficult for students. However, interventions have successfully resulted in improvements to scale estimations and improve exam performance [3]. We will discuss best practices for developing effective interdisciplinary teams, and how to overcome challenges of working across disciplines and across grade levels. We will provide examples of spatial interventions in scaling and penetrative thinking. [1] Hegarty et al. (2010) in Spatial Cognition VII 6222, 85- 94. [2] Uttal et al. (2012) Psychology of Learning and Motivation 57, 147-181. [3] Resnick et al. (2016) Educational Psychology Review, 1-15.