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Spatial skills are embedded in all aspects of the geosciences. The teaching and learning of spatial skills has been a challenging, but vital, endeavor. To support student learning of spatial skills in undergraduate courses, we designed scaffolds for spatially dependent content in a mid-level geoscience course using playdough to allow students to model and manipulate geologic structures and processes. Using a semester-long geology course as a case study, we explore the ways in which students reported playdough supported their learning of geoscience content during the course. Students found the playdough most helpful for visualizing geologic structures, such as faults and domes, which students were then able to encode into their long-term memories, or "mental libraries," for application to new contexts on assessments later. The playdough was more helpful at the start of the course when students were grappling with introductory course content and skills. Later in the course, the need for the playdough as a scaffold faded, as intended. Most students eventually sought new scaffolds, such as three-dimensional block models, which illustrate more complex and sophisticated structures and processes. Therefore, we see playdough as a useful scaffold for students in the early stages of spatial and geologic skill development as it aids students in developing both sets of skills. It is easy to utilize, inexpensive, portable, widely available, and familiar to most students.

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.

Spatial visualization is an essential prerequisite for understanding geological features at all scales, such as the atomic structures of minerals, the geometry of a complex fault system, or the architecture of sedimentary deposits. Undergraduate geoscience majors bring a range of spatial skill levels to upper-level courses. Fortunately, spatial thinking improves with practice, and students benefit from intentional training. Several promising teaching strategies have emerged from recent cognitive science research into spatial thinking: gesturing, predictive sketching, and comparison, including analogy and alignment. Geoscience educators have traditionally incorporated many of these tools in their teaching, though not always consciously, intentionally, and in the most effective ways. Our research team, composed of geoscientists and cognitive psychologists, has collaborated to develop curricular materials for mineralogy, structural geology, and sedimentology and stratigraphy courses that incorporate these strategies intentionally and purposefully, supporting student understanding of the spatially challenging concepts and skills in these courses. Collectively, these two dozen learning activities comprise the Spatial Thinking Workbook (http://serc.carleton.edu/spatialworkbook/index.html). Pre- to posttest gains on a suite of assessment instruments, as well as embedded assessments, show that these curricular materials boost students' spatial thinking skills and strengthen their ability to solve geological problems with a spatial component.

This manuscript addresses the potential role of geoscience education research in understanding geoscience expert practice. We note the similarity between the perception-action framework of Ulric Neisser (Neisser, 1976) and the observation-prediction framework used by geoscience practitioners. The consilience between these two approaches is that learning takes place when links are formed between predictions and observations and that this linkage is formed through conceptual models. Use of conceptual models facilitates learning at all levels; hence, there is little difference between learning in expert practice and student learning at all levels. The field of geoscience education is uniquely poised to enhance geoscience practice through investigation of expert learning, both in traditional field research and when experts adopt new tools and techniques. The consilience of expert practice, student learning, and cognitive science outcomes provides a rich opportunity to enhance both the intellectual merit of research and the direct and indirect broader impacts.

Spatial thinking skills are critical to success in many subdisciplines of the geosciences. We tested students' spatial skills in geoscience courses at three institutions (a public research university, a comprehensive university, and a liberal arts college, all in the midwest) over a two-year period. We administered standard psychometric tests of spatial skills to students in introductory geology, mineralogy, sedimentology and stratigraphy, hydrogeology, structural geology, and tectonics courses. In addition, in some courses we administered a related spatial skills test with geoscience content. In both introductory and upper level undergraduate geology courses, students' skills vary enormously as measured by several spatial thinking instruments. Additionally, students' spatial skills generally improve only slightly during one academic term, in both introductory and advanced geoscience classes. More unexpectedly, while there was a tendency for high-performing students to be adept at multiple spatial skills, many individual students showed strong performance on tests of one spatial skill (e.g., rotation) but not on others (e.g., penetrative thinking). This result supports the contention that spatial problem solving requires a suite of spatial skills, and no single test is a good predictor of "spatial thinking."

Geoscience instructors and textbooks rely on analogy for teaching students a wide range of content, from the most basic concepts to highly complicated systems. The goal of this paper is to connect educational and cognitive science research on analogical thinking with issues of geoscience instruction. Analogies convey that the same basic relationships hold in two different examples. In cognitive science, "analogical comparison" is understood as the process by which a person processes an analogy. We use a cognitive framework for analogy to discuss what makes an effective analogy, the various forms of analogical comparison used in instruction, and the ways that analogical thinking can be supported. Challenges and limitations in using analogy are also discussed, along with suggestions about how these limitations can be addressed to better guide instruction. We end with recommendations about the use of analogy for instruction, and for future research on analogy as it relates to geoscience learning.

Observational scientists use data—either qualitative or quantitative—to construct conceptual models but do not indicate which data are particularly important for the building of that model. We propose the use of salience to denote the relevance of data in supporting a particular model. Salience is effectively a weighting factor for observational data with respect to a model. We propose a scale to characterize the salience of data (from low to high): no attribution, negligible, peripheral, pertinent, important, and paramount. Data that are inconsistent with a given model are categorized with negative salience values. For fields in which the spatial distribution of salience can be visually displayed, we introduce the concept of salience maps. We provide an example of the use of salience rankings and construction of a salience map for the Sage Hen Flat pluton in the White Mountains in eastern California. The use of salience and salience maps is a way to provide increased reliability and trustworthiness of models, facilitate communication, promote inclusiveness, and allow for scientists to more effectively build off prior data in the observational sciences. [ABSTRACT FROM AUTHOR]