Representational decisions when learning population dynamics with an instructional simulation

Abstract

DEMIST is a multi-representational simulation environment that supports understanding of the representations and concepts of population dynamics. We report on a study with 18 subjects with little prior knowledge that explored if DEMIST could support their learning and asked what decisions learners would make about how to use the many representations that DEMIST provides. Analysis revealed that using DEMIST for one hour significantly improved learners’ understanding of population dynamics though their knowledge of the relation between representations remained weak. It showed that learners used many of DEMIST’s features. For example, they investigated the majority of the representational space, used dyna-linking to explore the relation between representations and had preferences for representations with different computational properties. It also revealed that decisions made by designers impacted upon what is intended to be a free discovery environment.