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One area of my research specifically aims to identify when and how students apply unique strategies to solve problems typically assumed to mandate high spatial ability. Because students cannot always directly perceive the three-dimensional phenomena under study in science, they are often asked to mentally manipulate internal representations constructed from two-dimensional diagrams for learning and problem solving. This practice suggests that visuo-spatial ability is fundamental to scientific problem solving. Our research has shown that this assumption neglects the role of domain-specific problem solving strategies that develop as scientific expertise grows. In fact, students do not rely exclusively on visuo-spatial ability to problem solve, but instead apply a wide range of algorithms and heuristics that allow them to transform diagrams to problem successfully (see Figure 1). This practice mirrors the practice of experts and strongly suggests that individual differences in visuo-spatial ability are poor predictors of scientific problem solving ability.A second area of my research focuses on the development and evaluation of novel educational technologies for teaching science, particularly chemistry. At all levels of instruction, chemistry deals with concepts and phenomena that are not directly perceptible to students. To help students reason about these phenomena, I develop and study models (both physical and virtual) that give students a molecular-level view of phenomena they experience in the everyday world. Using the Connected Chemistry Curriculum, I aim to offer students activities that support connections between the observations students make in the laboratory with the observations they make in multi-agent modeling environments. I study all aspects of learning and teaching with novel technologies and I am currently examining the manner in which new technologies improve representational competence and argumentation practices in the science classroom.
Figure 1. Two strategies for translating chemistry representations: mental rotation (top pathway) and a diagrammatic algorithm (bottom pathway).
- Stieff, M., & Raje, S. (2010). Expertise algorithmic and imagistic problem solving strategies in advanced chemistry. Spatial Cognition & Computation. 10(1), 53-81.
- Stieff, M. (2007). Mental rotation and diagrammatic reasoning in science. Learning and Instruction, 17(2), 219-234.
- Stieff, M. (2005). Connected Chemistry–A novel modeling environment for the chemistry classroom. Journal of Chemical Education, 82(3), 489-493.
B.S., Dickinson College, 1998
M.S., Northwestern University, 2000
Ph.D., Northwestern University, 2004