Models and simulations provide a visual expression of complex phenomena and processes, whether tangible (e.g., the greenhouse effect) or abstract (e.g., molecular motion). Models and simulations can be used as “virtual laboratories” when it is impossible, too difficult, too expensive, or unethical (for example, in the case of research with animals) to conduct a real experiment. Models and simulations enable learners to observe these complex and abstract processes in a concrete manner and to deepen their investigation of them, thereby offering support in such cases.
A model is an abstraction of a phenomenon under study. It represents selected aspects of the phenomenon and the relationships among them, with the aim of explaining, describing, predicting, or investigating a particular phenomenon, depending on the context in which it is studied. A model may include a computer-based interactive representation (for example, animations) of a scientific process that enables students to make different changes in order to investigate the phenomenon under study (for example, examining the relationship between the type of material and the temperature of light bulbs). A simulation can be understood as using a model in order to investigate the behavior and unfolding of a phenomenon and the processes involved in it (for example, time-dependent behavior or examining the formation of products that emerge as part of the phenomenon). The present principle addresses the use of simulations and models for investigating phenomena, rather than modeling processes that refer to the construction of models and simulations.
The purpose of investigating models and simulations can vary according to need, for example, becoming familiar with certain aspects of the phenomenon under study, explaining these aspects, or investigating them. Investigating a model or simulation involves “playing” with variables, for example by examining how the phenomenon behaves across a range of situations, including extreme cases, or by changing one variable at a time in order to test the unique influence of each variable on the system, and so on. This “play” is conducted without changing the overall structure of the model or simulation, but rather for the purpose of investigating it. Learners can benefit substantially from this type of investigation, especially when the visualization includes multiple representations that are linked to one another. In this way, learners can deepen their understanding of the phenomenon.
At times, a phenomenon or idea can be illustrated in several ways. For example, a model representing states of matter using chemical language (a symbolic representation), a model describing a depiction of matter in the physical world (a macro-level representation), and a graphical representation of the particulate model (a micro-level representation). Investigating these representations side by side may enable learners to identify the connections among representations and examine the differences between them, thereby deepening their understanding of the phenomenon under study.
In the “Radon Gas” Project, students investigate a simulation that describes the distribution of radon gas at different times, with the aim of learning about the radon gas phenomenon. Students sample from the simulation samples of a certain size that include radon gas level data across the day. They examine gas levels in different samples and learn how the gas may behave over the course of a day. “Playing” with the sample sizes drawn from the simulation enables students to refine their hypotheses regarding radon gas behavior, and also to learn complex statistical ideas related to randomness, sampling behavior, uncertainty, and more.
Deepening and Expansion ▼
The importance of computer-based illustrative tools in chemistry instruction
Levy and Wilensky (2009) describe chemistry content knowledge as comprising three knowledge components: the sub-micro level (atoms, molecules, and chemical reactions), the macro level (tangible and observable phenomena), and the representational level (symbols of chemical entities, chemical equations, and mathematical representations). Chemistry experts are proficient in these three knowledge components and move flexibly among them. Beyond the ability to move among these knowledge components, deep understanding of chemical phenomena involves mental simulation of a population of moving bodies, which depicts the diverse behaviors of many molecules and the pattern emerging from those behaviors. This mental model is foundational for understanding the particulate nature of matter and is necessary for understanding central ideas such as chemical equilibrium and chemical reactions. Levy and Wilensky (2009) describe a chemistry curriculum that externalizes the sub-micro component and encourages making connections among the three components. This curriculum draws, among other things, on investigating different models and representations of the system under study.
Studies show that using computer-based models that enable investigation of different graphical representations supports learning of the three knowledge components in chemistry and deepens understanding of chemical phenomena. This use is considered particularly effective when simultaneous, dynamic, and synchronous links are created among different representations, and when learners are encouraged to attend to differences and similarities among different representations of the same chemical phenomenon.
Core features of technology-based visualizations in learning
According to Kali and Linn (2008), in elementary and middle school classrooms, visualizations play a particularly important role because they make complex and abstract processes observable in a concrete manner. Technology-based visualizations may promote science learning. A technology-based visualization (or illustrative tool) is any interactive representation (including animations) of a scientific process that enables students to make changes in order to examine the phenomenon under study (for example, a virtual visualization that enables changing material types and examining the heat level of light bulbs composed of those materials).
For such tools to contribute effectively to science learning, they should support knowledge integration. That is, they should help learners integrate and construct knowledge in the context of the phenomenon under study by eliciting learners’ ideas, sorting and filtering these ideas, and creating new connections among them. Kali and Linn (2008) identified four principles that support integrating visualizations into the curriculum: (a) reducing visual complexity in order to enable learners to identify salient information in the representation; (b) scaffolding the process of generating explanations; (c) supporting learners in modeling processes of complex scientific phenomena; and (d) using multiple linked representations.
Guidelines for integrating technology-based visualizations into learning environments
According to Smetana and Bell (2012), computer simulations and models play an important role in science learning and instruction. In many cases, these tools contribute substantially to advancing science knowledge and skills, particularly when they involve students in authentic inquiry. At the same time, the benefit of using computer-based simulations depends on how they are embedded in the learning environment. The authors describe four research-based guidelines for integrating computer simulations into learning environments: (a) simulations should function as supportive tools for learning rather than as the primary focus; (b) simulations should be part of a structured learning process; (c) simulation use should encourage learners to engage in reflection; and (d) simulation use should create cognitive conflicts for learners.
Skills involved in interpreting and understanding visualizations
Gilbert (2005) presents the complexity of the term visualization (illustration) by noting two dictionary definitions: (1) creating a mental image—to imagine; and (2) making visible. Different visualizations may present information in tables, graphs, and more, but it is important to understand how learners interpret and what they understand from these visualizations (i.e., what mental image they form). To examine this question, particularly in science learning and teaching, Gilbert proposes the following framing: visualization refers to or generates a set of modes or sub-modes: (1) the material mode—referring to the material from which the visualization is made; (2) the verbal mode—referring to how the visualization’s components and their relationships are explained, as well as to the metaphors or analogies on which the visualization is based; (3) the symbolic mode—referring to symbols, expressions, or formulae involved in the visualization; (4) the visual mode—referring to graphs, diagrams, animations, and so forth presented in the visualization; (5) the virtual mode—referring to computer-based visualizations; and (6) the gestural mode—referring to bodily movements or gestures involved in the visualization.
These modes are often intertwined. Moreover, the use of technology enables the presentation and investigation of multiple representations, which are challenging to understand in isolation. Thus, learners—especially those learning science—need to engage deeply with the different modes while also moving among them, learning how to integrate them, and evaluating them and their roles. Therefore, they need to become metacognitive with respect to visualization; that is, they need to develop meta-visual capability.
Additional Resources:
Hans, M., Kali, Y., & Yair, Y. (2011). Developing spatial abilities by integrating knowledge technologies with simple physical means. In: G. Kurtz & D. Chen (Eds.), ICT, Learning and Teaching (pp. 101–124). The College of Academic Studies, Or Yehuda. Download
References ▼
Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In Visualization in science education (pp. 9-27). Springer, Dordrecht. (link)
Kali, Y., & Linn, M. C. (2008). Designing effective visualizations for elementary school science. Elementary School Journal, 109(2), 181-198. (link)
Kali, Y., (2006). Collaborative knowledge-building using the Design Principles Database. International Journal of Computer-Supported Collaborative Learning, 1(2), 187-201.
Levy, S. T., & Wilensky, U. (2009). Crossing levels and representations: The connected chemistry (CC1) curriculum. Journal of Science Education and Technology, 18(3), 224-242 (Link).
Smetana, L., & Bell, R. (2012). Computer Simulations to Support Science Instruction and Learning: A critical review of the literature. International Journal Of Science Education, 34(9), 1337-1370.