KDI: Visualization and Spatial Reasoning: Cognitive Models, Skill Acquisition and Intelligent Tutors

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Visualization and spatial reasoning are integral components of intelligent systems. They form the basis for understanding a wide variety of topics across science, mathematics and engineering, including molecular structures, topologies, motion and forces, and manufacturing processes. Historically, many students, especially female students, have had difficulty acquiring visualization and spatial reasoning skills, creating potential barriers to advancement in science, mathematics and engineering. Within engineering, faculty have found it both challenging and time consuming to teach topics that require strong visualization and spatial reasoning skills, topics such as product design, manufacturing, and engineering modeling and analysis. Similarly, engineering students have found these topics unmotivating and difficult to comprehend. With the advent of sophisticated computer graphics and animation, one might expect that the need for human visualization skills has been eliminated. But this is not the case. Computers cannot replace the need for these skills in science and engineering just as calculators have not replaced the need for quantitative skills. Thus this project has three goals: l) to advance our understanding of human visualization and spatial reasoning; 2) to use this knowledge to develop computer-based visualization instruction; and 3) to incorporate this instruction into intelligent multimedia tutors in ways that maximize their effectiveness for a broad mix of students while minimizing the development time and cost for the faculty involved. The achievement of such goals has required that we put together a team of researchers with backgrounds in psychology, education, engineering and computer science. Although visualization and spatial reasoning are fundamental cognitive skills, the cognitive processes that govern them are poorly understood. Thus, as our first goal, we will undertake during year l a series of experiments in our Eye Movement Laboratories designed to test alternative theories of how individuals represent mentally and reason spatially about 3-D objects and their transformations. We will use the detailed eye movement data as a window on the underlying cognitive processes. We have made similar use of such data in reading, visual search and scene perception (Rayner, l992, l998; Rayner & Pollatsek, l992). We expect these data to reveal large, stable differences among individuals, not only between low and high spatial ability participants, but also within groups of participants of similar spatial abilities.Visualization and spatial reasoning skills are critical to the understanding of many concepts within science and engineering. Yet, we have little understanding of how we can best teach these skills. Thus, as our second goal, we will develop during year 2 computer-based visualization skills instruction modules based on what we have learned during the first year about the problems that individuals have and the strategies that work successfully, modules that will take advantage of current advances in instructional theories and technologies. Having developed the modules, we will then conduct a series of experiments in the second year designed to test theoretically motivated methods for delivering visualization instruction that improve the content of the instruction delivered to high and low spatial ability learners, optimize the mix of part- and whole-task training, and maximize the number of individuals that develop expertise.