As cognitive scientists, our goal is to develop empirically grounded cognitive models (both theoretical and computational), that can explain various aspects and mechanisms of learning scientific phenomena, such as misconceptions, conceptual development, mechanistic reasoning, etc. Specifically, our research predominantly looks at the following:
Knowledge Representation & Conceptual Change
Perhaps one of the oldest questions concerning the human mind is how knowledge is represented in the human mind, and how our conceptions change over time. Our research in this area focuses on identifying learners' schematic knowledge structures, their interactions and evolution, all of which constitute their sense-of-mechanism (diSessa, 1993) of various physical, biological and social phenomena. We investigate these issues primarily in the context of learners' interactions with computational representations of these phenomena.
While the general area of analogical reasoning and transfer has been a favorite and contentious topic of research in the field for many years (Gick & Holyoak, 1983; Schwarz & Bransford, 1998; Thorndyke & Woodworth, 1901; Barnett & Ceci, 2002; Hammer, Elby, Scherr & Redish, 2004; etc.), it is only recently that cognitive and learning scientists have started investigating these issues in the context of multi-agent based models (Goldstone & Son, 2009a, 2009b; Goldstone & Wilensky, 2008). Our goal is to significantly extend this literature by developing theoretical frameworks for understanding how transfer of knowledge takes place (and/or can be facilitated) when learners interact with multi-agent based models of physical phenomena.
Research Overview >