Theoretical Themes

Research on spatial skills and spatial learning has suffered from fragmentation across a variety of isolated subfields. For example, spatial language researchers rarely interact with researchers studying maps and diagrams, and neither community interacts much with researchers investigating individual differences in fundamental spatial skills.

Fragmentation also exists across disciplines, with researchers in cognitive psychology, artificial intelligence and education often being unaware of relevant work in the other discipline. To overcome this fragmentation and synthesize the set of ideas needed to create the science of spatial learning, SILC involves researchers from multiple subfields, disciplines, and institutions who are committed to an integrative research approach. In addition, the Center consists of researchers with a substantial commitment to education: half of our 24 core faculty either currently hold positions in an Education department, or have degrees in Education.

Cross-Cutting Theoretical Themes

One of the reasons that research on spatial skills and spatial learning has been fragmented is that prior investigators have not delineated over-arching themes that unite research in various disciplines and foster communication about interdependencies and synergies. To support synthesis, SILC has identified five theoretical themes that cut across projects and that serve to unify Center initiatives and motivate our selection of research projects.

Qualitative/quantitative integration:  Space is continuous, and many aspects of spatial understanding involve metric, analog representations. However, human spatial representations also include symbolic, qualitative representations that are grounded in non-symbolic representations, which provide a bridge between perceptual and conceptual knowledge and skills. Findings in cognitive psychology on combining coordinate and categorical representations have shown the importance of spatial categories in perception and memory (e.g., Huttenlocher, Hedges & Vevea, 2000), providing evidence that such representations are fundamental to human spatial cognition. Work in artificial intelligence has demonstrated that qualitative spatial representations are useful for modeling a wide range of engineering and scientific reasoning (e.g., Forbus, Nielsen & Faltings, 1991). Such results provide evidence that qualitative spatial reasoning is central to STEM education.

Organizing schema for spatial skills:  Based on cognitive, linguistic and neuroscientific data (e.g., Chatterjee, 2008), we distinguish among different types of spatial skills. First, we note that objects have intrinsic structure that defines properties like their shape and size, and that they also have extrinsic relations to other objects and/or external reference systems (e.g., a mountain is located in a particular geographic region and directly west of some other mountain). Second, we note that both intrinsic and extrinsic spatial relations can be represented statically but can also be dynamically transformed, either in physical actuality or through mental simulations. Thus, there are four broad categories of spatial skills, defined as intrinsic-static, intrinsic-dynamic, extrinsic-static and extrinsic-dynamic. These categories give us an organizing schema for thinking about what spatial skills might be important to STEM and what skills might need to be assessed and worked on. It transcends prior work on ways to think about individual differences, which has been inconclusive, largely because it has been divorced from theory (Hegarty & Waller, 2005).

Importance of external symbol systems:  Spatial language, diagrams, graphs, and maps are widely used in STEM practice, and research suggests that, used properly, these can be powerful aids to learning. But these systems are not automatically powerful learning aids; their effective use needs to be taught. For example, middle school students, and their teachers, often ignore diagrams simply because they do not know how to interpret them. Understanding how external symbol systems function in human cognition is crucial to using them effectively in education.

Spatial analogy as a key learning mechanism:  Analogy is known to be a powerful general learning mechanism (Gentner, Ratterman & Forbus, 1993). Many people think of analogy as only involving abstract, conceptual representations, but concrete spatial analogies are widely used in spatial learning. For example, we are finding that within-domain analogies, such as having students compare photographs, can be useful in learning geological concepts. Our computational model of sketch understanding uses analogical reasoning over qualitative spatial representations to enable it to perform at human levels and in human ways on tasks such as Ravens’ Progressive Matrices. Finally, analogical processes are crucial in applying spatial representational tools to STEM topics, such as when topographic maps are aligned with the corresponding physical space.

Learning from action to abstraction:  In contrast to traditional views of the mind as an abstract information processor, recent theories of embodied cognition suggest that our representations of objects and events are often grounded in the sensorimotor systems we use to perceive and act on the world (Wilson, 2002). This linking of thought and action is readily observed when STEM practitioners talk about objects in their area of expertise. For example, organic chemists gesture heavily when discussing molecular structure and engineers rely on sketches in conceptual design. This leads us to believe that involving the action systems in learning may help to deepen students’ knowledge of abstract concepts by tying them to sensorimotor brain systems that are good at capturing spatial/action relationships. We are interested in understanding how performing different actions (ranging from feeling forces when learning about angular momentum in physics, to actually manipulating models of physical molecules in chemistry to learn about their spatial makeup, to sketching spatial relations in geosciences) might bolster spatial learning by engaging sensorimotor systems that might not otherwise be brought to bear on the concepts at hand. Moreover, we are interested in when this action information might harm performance by tying students’ representations too closely to the physical world and how tools such as gesture and sketching might serve as a bridge between concrete physical relations and more abstract knowledge. Finally, we are interested in how different forms of action can provide a window into learners' minds by revealing information that they may not be able to articulate verbally.

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