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Understanding and communicating uncertainty is a key skill needed in the practice of science. However, there has been little research on the instruction of uncertainty in undergraduate science education. Our team designed a module within an online geoscience field course which focused on explicit instruction around uncertainty and provided students with an uncertainty rating scale to record and communicate their uncertainty with a common language. Students then explored a complex, real-world geological problem about which expert scientists had previously made competing claims through geologic maps. Provided with data, expert uncertainty ratings, and the previous claims, students made new geologic maps of their own and presented arguments about their claims in written form. We analyzed these reports along with assessments of uncertainty. Most students explicitly requested geologists’ uncertainty judgments in a post-course assessment when asked why scientists might differ in their conclusions and/or utilized the rating scale unprompted in their written arguments. Through the examination of both pre- and post-course assessments of uncertainty and students’ course-based assessments, we argue that explicit instruction around uncertainty can be introduced during undergraduate coursework and could facilitate geoscience novices developing into practicing geoscientists.

 

Comprehending 3D diagrams is critical for success in scientific practice and research demonstrates that understanding of 3D geology diagrams can be improved by making predictive sketches. In mathematics, explaining erroneous examples can support learning. This study combined these approaches to better understand how to effectively support 3D geologic diagram understanding. Participants generated sketches, explained erroneous example sketches, or copied and explained correct sketches. It was hypothesized that generating sketches or explaining erroneous cases would improve understanding, but an open question was whether these conditions would differ from each other. Explaining erroneous examples and sketching improved understanding whereas explaining correct sketches did not. Further, explaining erroneous examples was a more efficient strategy than sketching. These results indicate that erroneous examples can be effective for supporting 3D diagram comprehension and may be a practical substitute for some traditional sketching activities in the context of real classrooms where class time is limited.

 

The field of geology is poised to make a fundamental transition in the quality, character, and types of science that are possible for practitioners. Geologists are developing data systems—consistent with their workflow—to digitally collect, store, and share data. Separately, geologists and cognitive scientists have been working together to develop tools that can characterize the level of uncertainty of both data and models. The transformational change comes from the simultaneous combination of these two approaches: digital data systems designed to capture and convey scientific uncertainty. This approach promotes better data collection practice, improves reproducibility, and increases trust in community-based digital data. We applied these methods—attending to uncertainty and its incorporation into digital repositories—to the Sage Hen Flat pluton in eastern California, USA, where two published maps provide different interpretations. Incorporating uncertainty into our workflow, from field data collection to publication, allows us to move beyond binary choices (e.g., is this data/model right or wrong?) to a more nuanced view (e.g., what is my level of uncertainty about the data/model?) that is shareable with the larger community. 

Abstract Field geologists are increasingly using unmanned aerial vehicles (UAVs or drones), although their use involves significant cognitive challenges for which geologists are not well trained. On the basis of surveying the user community and documenting experts’ use in the field, we identified five major problems, most of which are aligned with well-documented limits on cognitive performance. First, the images being sent from the UAV portray the landscape from multiple different view directions. Second, even with a constant view direction, the ability to move the UAV or zoom the camera lens results in rapid changes in visual scale. Third, the images from the UAVs are displayed too quickly for users, even experts, to assimilate efficiently. Fourth, it is relatively easy to get lost when flying, particularly if the user is unfamiliar with the area or with UAV use. Fifth, physical limitations on flight time are a source of stress, which renders the operator less effective. Many of the strategies currently employed by field geologists, such as postprocessing and photogrammetry, can reduce these problems. We summarize the cognitive science basis for these issues and provide some new strategies that are designed to overcome these limitations and promote more effective UAV use in the field. The goal is to make UAV-based geological interpretations in the field possible by recognizing and reducing cognitive load. 

Abstract. In the geosciences, recent attention has been paid to the influence of uncertainty on expert decision-making. When making decisions under conditions of uncertainty, people tend to employ heuristics (rules of thumb) based on experience, relying on their prior knowledge and beliefs to intuitively guide choice. Over 50 years of decision-making research in cognitive psychology demonstrates that heuristics can lead to less-than-optimal decisions, collectively referred to as biases. For example, the availability bias occurs when people make judgments based on what is most dominant or accessible in memory; geoscientists who have spent the past several months studying strike-slip faults will have this terrain most readily available in their mind when interpreting new seismic data. Given the important social and commercial implications of many geoscience decisions, there is a need to develop effective interventions for removing or mitigating decision bias. In this paper, we outline the key insights from decision-making research about how to reduce bias and review the literature on debiasing strategies. First, we define an optimal decision, since improving decision-making requires having a standard to work towards. Next, we discuss the cognitive mechanisms underlying decision biases and describe three biases that have been shown to influence geoscientists' decision-making (availability bias, framing bias, anchoring bias). Finally, we review existing debiasing strategies that have applicability in the geosciences, with special attention given to strategies that make use of information technology and artificial intelligence (AI). We present two case studies illustrating different applications of intelligent systems for the debiasing of geoscientific decision-making, wherein debiased decision-making is an emergent property of the coordinated and integrated processing of human–AI collaborative teams.