Galileo (1564- 1642), was the one who set the foundations for studying real-world systems indirectly through reduced and idealised models, from where we can gather valid descriptions, explanations and predictions about the systems (Halloun, 1996). Furthermore, models are considered important since we humans cannot understand many aspects of the world, because there might be some things going on that we do not know. So, we choose to construct simplified models in order to represent anything we do know from our experiments (Ogborn, 1994).
Still models are thought as human constructs, used to provide a representation for the mechanism of the natural phenomena in a coherent way (Louca and Constantinou, 2002). What’s more, models are used because the physical and natural world that we try to study in science is a complex world and to understand it better we break it down into manageable parts and represent it with models (Frost, 2003). As Giere (1988, p. 64) indicates, “the model-reality adjustment is not overall, but rather relative to those aspects of the world that the models attempt to capture”.Order now
Consequently, models are considered to be “representations of a real-world process or thing”, used to simplify the phenomenon and make it more understandable (Glynn and Duit, 1995, p. 3). Modeling in Science Education Considering all the above and having in mind the powerful potential of models and modeling process, many educators and researchers present their use in science education as essential and core (e. g Louca and Zacharia, 2008, Louca et. al. , 2003, Papaevripidou et al, 2006, Schwartz & White, 2005), since they can facilitate and promote science learning and teaching (Grosslight et al, 1991).
Therefore, they are identified as vehicles for learning the world (Stanford Encyclopaedia of Philosophy, 2006). Gilbert (1993), recognises models as “one of the main products of science,” important “element in scientific methodology,” and “major learning tools in science education” (pp. 9-10). Specifically, Hudson (No date) emphasizes the importance of modeling in science by indicating that without modeling practices it is not possible to visualise effective teaching.
Moreover, investigators (e. g. , Snir et al., 1988) who have talked with students about the nature of models assumed that the enrichment of students’ conceptions about the nature of models could support student’s learning from models (cited in Grosslight et al. , 1991). Also, models in science are thought to be the “way we construct understanding about the physical world and therefore are an anticipated result of teaching about science” (Louca and Constantinou, 2002). Consequently, lot of researches were conducted in the field of models and modeling in science education and several reasons that make the use of models in science education vital were revealed.
In order to understand the need for using models in science teaching it is useful to define what model-based teaching is and as Gobert and Buckley (2000) points out “model based teaching is any implementation that brings together information resources, learning activities, and instructional strategies intended to facilitate mental model-building both in individuals and among groups of learners” (p. 892). Moreover, Popper (1982) indicates that if science is the art of oversimplification, then models are the tools for organizing and simplifying things (cited in Sizmur and Ashby, 1997).
Likewise, models in science are considered to be scientists’ and teachers’ attempts to represent every day phenomena that are difficult and complex to understand in order to support their students’ learning (Harrison and Treagust, 1998). The various models that can be generated in early school years are conditional representations of phenomena, explaining aspects of reality that are organised progressively, thereby leading to the development of these models (Acher et al. , 2006).
As Osborne and Hennessy (2003) add, models can be very supportive, in order to understand the complex and confusing real world, since they give the opportunity to students to focus on specific concept and isolate variables they want to examine. In addition, modeling is recognised as part of a scientist’s daily life (Zhang et al, 2005) and it is quite vital for students to be able to work and think like scientists (Wilensky and Reisman, 2006). Consequently, there is a growing interest in developing pedagogic models in science education, that help students understand how scientists conceptual physical phenomena (Louca, 2004).
When children are learning science the task is similar with scientists’: to “progress from a directly experienced realm of things and events toward more theoretical explanations” that help students understand objects and processes that cannot observe by themselves (Sizmur and Ashby, 1997, p. 7). So, by engaging students with similar practices with those of scientists’ such as modeling, helps them to construct knowledge and achieve epistemological understanding (Gobert and Buckley, 2000).
Pollak (1994) argues that unless students are “introduced to the game that professional scientists play called ‘creating and shooting down models’ let them in on the game of ‘being’ a scientist” (p. 91). Additionally, the National Science Education Standards emphasizes this by stating that “all students should develop an understanding of the nature of science” and that this understanding includes knowledge that “scientists formulate and test their explanations of nature using observation, experiments, and theoretical and mathematical models” (National Research Council, 1996, p.171).
However there is difference between scientists’ and students’ perceptions about models, since scientists think of a model as “a set of assumptions that include theoretical entities and relations among them, that are designed to help them think about how to explain some aspect of reality” (Snir et al. , 2000, p. 797), while students assume that models are exact representations or pictures of reality (Grosslight et al. , 1991).
What’s more, modeling-based teaching can be beneficial since the development and refinement of models can have qualitative outcomes of understanding different concepts and the nature of science as well as gaining procedural and reasoning skills (Grosslight et al, 1991; Harrison and Treagust, 1998). Additionally, previous research showed that by engaging students’ modeling skills the achievement of science process skills is also accomplished (Rubin and Norman, 1992).
Also, by using models students can gain scientific knowledge meaningfully, something that supports the development of scientific skills, especially critical thinking (White, 1993). Also, it is considered that learning in science can be supported through the construction of models for physical phenomena and knowledge about those models, will “permit learners to further use, test and revise their models in the light of new evidence” (Louca and Constatntinou, 2002, p. 15), something vital in the filed of science.
Various studies in the area of modeling-based learning revealed that the use of models as tools for observing, exploring, synthesizing and predicting, provides a learning environment where students can build, test, revise and apply models (Papaevripidou et al. , 2006, Schwartz & White, 2005). Moreover, the experience of any modeling-based learning gives the opportunity to students to think and talk scientifically about natural phenomena (Penner, 2001), to share, discuss and criticize (Devi et al. , 1996; Rowette et al. , 2000) their ideas as well as reflect upon their understandings (Gilbert et al., 1998).
Additionally, there has always been a challenge in science education for teaching students complex systems and unobservable phenomena. Therefore, models can facilitate the representation of this kind of phenomena and support students’ conceptual understanding. Specifically, exploratory modeling activities, which allow students to interact with already constructed models, can explore phenomena that are not accessible to direct observation and the outcome can be a qualitative understanding of complex processes [(Feurzeig & Roberts, 1999) cited in Stylianidou et al.