Our research investigates the relative effectiveness of concrete and virtual models for developing students’ representational competence in the domain of chemistry. Kozma & Russell (2005) define representational competence as ‘a set of skills and practices that allow a person to reflectively use a variety of representations or visualizations, singly and together, to think about, communicate, and act on chemical phenomena in terms of underlying, aperceptual physical entities and processes’. Previous research in the domain of organic chemistry, including our own, showed that models have the potential to support students as they develop skills in drawing, interpreting, and translating between multiple representations, such as diagrams, formulae, and models. These skills are essential to a student’s growth as a chemist.
In our past research (Padalkar & Hegarty, 2012; Stull, et al., 2010; Stull, Barrett, & Hegarty, 2013; Stull & Padalkar, 2012), we have demonstrated that concrete models are highly effective when students are asked to translate between different diagrams, a common and important task in chemistry. We hypothesize that translating between organic chemistry diagrams is more accurate when concrete models were used in the translation process because models allow difficult mental processes to be augmented by external actions on the models. For example, it is often easier to manually rotate a complex object than to accurately mentally rotate one. By performing actions in the world to support cognition, students are able to better manage difficult cognitive tasks. Translating between diagrams of molecules can involve imagining the 3D structure of the molecules by decoding the conventions of the 2D diagrams and either mentally rotating these 3D structures or imagining them from different perspectives. Using a model in this instance allows an external representation of the 3D structure that can be physically rotated, obviating the need for effortful mental rotation. (NSF #0722333 & #1008650; Spencer Foundation)
In our current research, we attempt to understand what happens after students have learned to use the models. This is to say, do models scaffold learning and help a student to develop from a concrete to an abstract reasoner or do models become a crutch that limits a student’s development? We also seek to understand the benefit of actively manipulating models during the course of learning as opposed to merely viewing the results of manipulating a model. In addition, we are exploring the importance of different perceptual cues when learning with models. This knowledge will inform both the design of concrete and virtual learning resources as well as the principles and practices that support meaningful learning when using models. (NSF #1252346)