CHALLENGES TO REASONING AND LEARNING ABOUT 3D SPATIAL RELATIONS: BRIDGING THE GAP BETWEEN LAB-RESEARCH AND FIELD-BASED EDUCATION

The talk will review findings from a program of cognitive science research on aspects of spatial cognition that are important for success in learning and practicing in the geosciences. We will discuss recent findings and strategies that could be adapted to field settings to support reasoning about 3D spatial relations. The talk will consider three types of 3-D reasoning challenges: navigating in the field, spatial reasoning using topomaps, and reasoning about cross sections in geological maps. Work on navigation strategies in a virtual suburban campus indicates there are three broad categories of navigators: those who can learn specific locations well but only when they stay on the same route; those who can learn locations well and can integrate those locations across multiple routes; and those who can do neither. Learning locations within a route and integrating across routes appear to be supported by separate cognitive abilities (e.g., working memory, and navigation strategy selection), suggesting that improving students’ navigation may benefit from an individually-tailored approach. Independent of memory for objects and routes, students need to be able to represent spatial relations using maps. Generally, students learn to locate themselves, record observations, and reason about large-scale spatial relations using topomaps. Recent work suggests two critical areas for support of new topomap users: learning to recognize important patterns and learning to reason about the metric meaning of the iso-elevation contours. We find that attentional guidance can support the visual learning challenge of recognizing, or appropriately processing, informative patterns, and that providing verbal conceptual categories can support both reasoning about elevation and learning to recognize/process visual patterns. Finally, in research on reasoning about spatial relations inside the Earth, we have found that predictive sketching of cross sections can effectively teach students to better reasoning about 3D structures in block models. The talk will present a theoretical framework for learning based on student-generated predictions to offer an indication of how one might bring these lab findings out into the field.