Representational decisions when learning population dynamics with an instructional simulation

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 dynalinking 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

Multiple forms of dynamic representation

The terms dynamic representation and animation are often used as if they are synonymous, but in this paper we argue that there are multiple ways to represent phenomena that change over time. Time-persistent representations show a range of values over time. Time-implicit representations also show a range of values but not the specific times when the values occur. Time-singular representations show only a single point of time. In this paper, we examine the use of dynamic representations in instructional simulations. We argue that the three types of dynamic representations have distinct advantages compared to static representations. We also suggest there are specific cognitive tasks associated with their use. Furthermore, dynamic representations of different form are often displayed simultaneously. We conclude that to understand learning with multiple dynamic representations, it is crucial to consider the way in which time is displayed

Learning with Multiple Representations:Supporting students’ translation between representations in a simulation-based learning environment

This paper reports a study which examined support the translation between multiple representations in simulation-based learning. We compared three versions of the same simulation-based learning environment: a learning environment with separated representations (control condition), a learning environment with dynamically linked representations, and a learning environment with integrated representations. Ninety learners from four middle vocational training schools (aged 16 to 18) took a pretest on an applied physics domain called ‘moment’, worked with a simulation-based learning environment on the domain, and took a posttest. Subjects were randomly assigned to one of the three experimental conditions. Subjects received an electronic questionnaire five times while working with the learning environment. This questionnaire asked subjects to score their experienced difficulty. The results of this study did not lead to significant results between conditions and thus does not lead to insights into the benefits or drawbacks of a particular measure to support translation. Insights into how the design of the learning environments may have influenced these results as well as research designs are discussed. Implications for future research will also be addressed

Models as mindtools for environmental education: How do students use models to learn about a complex socio-environmental system?

Environmental issues are complex and understanding them involves integration of different areas of knowledge, feedback and time delays, however strategies to cope with complexity are not often used or taught in environmental education. The aim of this thesis is to examine the benefit of three such strategies for environmental education: multiple external representations, learning from models, and collaborative learning. The socio-environmental system modelled was visitor impact in a national park in Australia. Students in Year 9 and 10 from two schools were given a text description (Text group) and either a system dynamics model (SDM group), an agent-based model (ABM group), or both models (SDM & ABM group). This experimental design allowed learning outcomes (environmental and system dynamics knowledge, and understanding of the socio-environmental system) and use of the model(s) (in terms of the proportion of time spent on each screen, activities, and strategies) to be compared in each learning environment (individual and collaborative). Multiple external representations were the most successful strategy in the individual learning environment in terms of increases in environmental knowledge. However, students given only the system dynamics model had greater understanding of the system, and students given only the agent-based model increased environmental knowledge easily identified in the animated representation. Prior knowledge, patterns of use, strategies for changing variables and the representational affordances of the models explained some of these differences. In particular, prior knowledge was an important indicator of how students coordinated use of the models in the SDM & ABM group. Learning with a system dynamics model was the most successful strategy for students in the collaborative learning environment. Differences between the learning environments were detected in all groups with respect to both learning outcomes and use of the models due to prior knowledge, interrogation of the models, and the learning environments themselves. These experiments have provided evidence that strategies for understanding complex systems provide viable methods of communicating complex ideas to school-aged students with varying levels of prior knowledge. In particular, multiple external representations provided students with flexibility in how they learned; models allowed students to experiment with a system otherwise not allowed; and a collaborative learning environment facilitated students’ interpretation of a system dynamics model