Frictionless virtual world

When teachers design learning activities to help their students understand complex systems and the way they function and change, model-making activities with computers can play a supportive role since students can construct their own understandings through them (Riley, 1990). As Osborne and Hennessy (2003, p.23) claim “research suggests that using computer modeling and simulation allows learners to understand and investigate far more complex models and processes than they can in a school laboratory setting”.

Moreover, researchers found that the use of computer models in educational subjects might provide opportunities for students to promote their understanding of unobservable phenomena in science (De Jong et al., 1999; Stratford, 1997). Additionally, computer modeling can make some scientific material more accessible and interesting (Papert, 1980), since computer simulations, along with other model-based teaching strategies could be a “powerful combination for supporting students’ visualization of unobservable phenomena” (Tray and Khan, 2007). Still, this visualisation of scientific concepts and complex systems is significant in science education since it enables students to resolve conceptual and reasoning difficulties they might have when studying complex phenomena (Louca and Zacharia, 2008).

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According to the above a computer-based program, if it is used properly, can facilitate science teaching of complex phenomena by supporting the construction of powerful models. A modeling program allows students to create their own models of a specific system, making comparisons with the real world, manipulating the model according to his data and therefore having complete control of the program (Laurillard, 2002).

According to Louca and Constantinou (2002), a computer-based tool allows user to create powerful models, representing a physical system and making predictions in unknown contexts. Also, as they continue, computer programming can provide friendly environments that students can manipulate in order to construct or refine models. Specifically, a computer-based modeling tool creates an open-ended, dynamic, and exploratory learning environment that supports the construction of representation of complex phenomena or natural systems through the coincident use of various procedures, having as a purpose to exceed static representations and move to dynamic representations of cause-effect relationships between variables (Sins, et al., 2005).

What’s more, “the constructivist perspective advocates high quality visual and auditory interfaces to simulate complex and authentic situations”. Therefore multimedia should “help learners construct their understandings by establishing links between what they know and familiar or not so familiar situations that appear realistic because of the high quality depiction available”(Rodrigues, 2006, p.2). However there might be a hidden danger of creating misconceptions by taking animations and images of real concepts too literally (Osborne and Hennessy, 2003).

Hennessy and O’Shea (1993) give a very good example for this when referring to the creation of a “frictionless virtual world”, which although it can be powerful in many ways, the fact that students have experience with both the natural world and the simulations, can create conceptual difficulties. Therefore, “there is a gap to be bridged, between students’ perceptions of real world prototypes and their computer based modeling activities” (Riley, 1990, p.258). Indeed, the influence of symbolic or representational learning materials is not clear (Betrancourt and Tversky, 2000 cited in Rodrigues, 2006). So, because of the increasing dependence on computer-based teaching resources (Lagowski, 2005), it is necessary to understand what engages pupil’s interest when using multimedia.

Another advantage of computer-based modeling is that computer requires from the modeler to discover its knowledge, while it gives him the opportunity to evaluate his model or a given model and therefore improve it through “re-expression” (Millwood and Stevens, 1990, p.249). According to Carmichael (2000), when the modeler manipulates the computer model through the interaction of the computer model and the reflected mental model, the modeler’s understanding of the nature is likely to change. As a result, modeler ends up to a complete and efficient model, representing a phenomenon with a complete and coherent way.

When moving on, another powerful potential of using computer-based tools in the modeling procedure is recognized. In some cases the program itself becomes the scientific model, where through the programming language the scientific model is designed, that’s when the outcome is someone to gain concrete understanding about the scientific phenomenon under study (Louca et al., 2003). Furthermore, by constructing models students deconstruct their understandings about particular physical mechanisms in small programmable pieces of knowledge, in order to translate a scientific idea in particular program code (Louca, 2004). As a result, through programming students might overcome any difficulties they usually obtain in science learning, like understanding the relationship between a scientific model and the real world (Louca and Zacharia, 2008).

Above and beyond, there are a lot of factors that need to be thought when a computer-based modeling approach is used. On factor is teacher’s role that might be considered as a key role in the process of science modeling, since they are the co-ordinators of the whole procedure and the accomplishment of successful understandings in science lessons depends on their approach. A research, concerning science teachers’ transformations of the use of computer modeling in the classroom (Stylianidou et al., 2004), indicated that there are a lot of factors influencing the use of modeling and simulations by teachers, like their confidence and competence, the availability of resources and the time constrained. For that reason there is need for the teachers to be educated to this domain, in order to reach into an effective science lesson by using modeling and simulations or generally to respond in every innovation in school science.

Millwood (1990) supports that generally the purpose of modeling is usually based on the approaches used rather than learner’s motivations and expectations. Likewise, by having a wide range of different computer-based programming environments, specifically developed for young learners, it is necessary to define which characteristics meet learners’ programming needs and learning habits in science (Louca and Zacharia, 2008), in order to select the appropriate computer tool for a science lesson.

On the other hand, Riley (1990) suggests that the challenges should be educational rather than technical and that “system dynamics” might “loose some of its mystique as change becomes easier to manipulate and explore on the computer screen” while the “notions of dynamics may supplant the static models we tend to keep in our minds and to use in our daily lives” (p.262). Therefore, for the embedment of a modeling approach in science teaching is important to consider possible factors that might affect students’ learning, according to their individual differences.

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