Modelling Environments

Modelling environments, as with immersive environments, are defined in a variety of ways in the literature. Ören (2011), for example, reports over 100 of these definitions. According to Bandini et al. (2009), ‘The term computer simulation is related to the usage of a computational model to improve the understanding of a system's behaviour and/or to evaluate strategies for its operation, in explanatory or predictive schemes’ (p. 1). Computer modelling is the process by which a computer is used to develop a mathematical model of a complex system or process. Such a model can be used to understand and clarify historical data in better ways, to predict future behaviour or to make decisions based on the likelihood of anticipated outcomes (The Models of Infectious Disease Agent Study (MIDAS), n.d.).

Computer simulations and modelling differ from virtual reality. Brey (2008) states that the aim of computer simulations usually is not to undertake realistic visual modelling of the systems they simulate, unlike in virtual reality. Instead the graphical representations usually include only the features that are relevant for the purposes of the simulation. Another difference is that computer simulations do not need to be interactive; typically,

the user will determine a number of parameters at the beginning of a simulation and then run the simulation without any further involvement in the process (Brey, 2008). Computer modelling is increasingly used in education and training. In science education, for example, computer modelling approaches have been used in several educational research projects (Gobert et al., 2004; Jacobson & Kozma, 2000; Wilensky & Reisman, 2006) to help school students understand complex systems in different fields in the sciences, such as physics and biology. They have shown to be successful at helping students develop a deep understanding of evolving phenomena (Dickes, Sengupta, Farris, & Basu, 2016; Sengupta, Kinnebrew, Basu, Biswas, & Clark, 2013). However, there has been very few studies on the use of modelling environments in primary teacher education programs to teach preservice teachers science concepts and their effect on preservice teachers’ science CK and confidence in science. As with immersive environments, studies using modelling environments with preservice teachers during their education program are more focused on preparing them to use computer modelling in their classrooms in the future (Schwarz et al., 2007).

Agent-based modelling is a computer modelling approach that has been used to simulate different types of complex systems utilising various platforms available to facilitate the development of models of these systems (Bajracharya & Duboz, 2013; Bandini et al., 2009). Different agent-based platforms exist and the current study used an agent-based modelling environment developed using one of these platforms: NetLogo. NetLogo is a programming language and modelling environment used commonly in both educational and research contexts (Wilensky, 1999) to simulate complex natural and social phenomena (Tisue & Wilensky, 2004). It has been shown to be a beneficial tool for learning about scientific phenomena in many fields including physics, chemistry, biology, economics, sociology, engineering and psychology (Blikstein et al., 2005). Railsback,

Lytinen, and Jackson (2006) reviewed five agent-based models and concluded that NetLogo was the highest-level platform affording a simple powerful programming language, extensive documentation and integrated graphical interfaces. In regard to appearance and usability, NetLogo is a user-friendly platform (Railsback et al., 2006).

NetLogo includes many examples and samples that teachers can use to support students in visualising complex phenomena. Because of this, it is argued that teachers can always find an example to suit their particular learning and teaching purposes (Niazi & Hussain, 2009). NetLogo has several features that make it a powerful platform for learning and teaching contexts. In terms of usability, it has been used in several studies in education because it is simple to download and use even for non-programmers, who can then progress quickly; is free; and has a large library of pre-existing models, which gives users the opportunity to explore the variety of models that can be created using this modelling environment and select an appropriate type for their context (Gammack, 2015). Thus, both students and educators can use it without the need for strong programming skills (Kanjilal, Rajgire, & Jain, 2013). In addition, NetLogo can be programmed to simulate natural and social phenomena and is especially suitable for modelling complex systems that changeover time (Allen & Davis, 2010; Wilensky, 1999).

Tisue and Wilensky (2004) identify growing acceptance of NetLogo in research and education as a valid modelling platform. It has been used for modelling and simulating diverse complex systems, including biological and social systems, because of its ability to provide visual simulation. In NetLogo, the user can set up simulations via an interface that requires minimal coding, after which the outcomes can be observed (Niazi & Hussain, 2009). No technical knowledge is required from users to explore the models. This makes NetLogo an exciting technique for teaching. It is easy for both teachers and

students to design and run simulations as it can be learnt and used by novices (Blikstein et al., 2005). NetLogo allows users to control parameters before and during a model run through a ‘slider’ provided on the interface page that can be adjusted to the desired model variable (Railsback et al., 2006). It is also a useful research tool and is appropriate for diverse learners (Tisue & Wilensky, 2004).

NetLogo is increasingly used in ecological and environmental modelling and has become a recognised tool in this area (Thiele, 2010). Different research has demonstrated learning improvements in science and science-related areas using computer modelling systems in both schools and higher education (Blikstein & Wilensky, 2008, 2010; Jacobson et al., 2016; Scarlatos, Courtney, & Tomkiewicz, 2014; Sengupta & Wilensky, 2009; Thompson & Reimann, 2006; Wilensky & Reisman, 2006). NetLogo was selected for this study as it runs across a number of platforms and is user friendly, thus making it accessible for preservice teachers. The focus of the study was on science knowledge, not on developing ICT skills as such.

In addition to the potential learning affordances of immersive and modelling environments, the authenticity and design of a ‘meaningful and pedagogically sound activity’ are essential factors to successfully exploit immersive environments in teaching and learning (Mamo et al., 2011) and facilitate the transfer of knowledge and skills gained in these environments to the real world (Kennedy-Clark, 2011). Thus, in addition to the technology learning resources, attention should also be given to the design of activities and tasks supported and facilitated by the technology used. As this study examined the development of preservice teachers’ CK in science, the selected immersive (Omosa) and modelling (Omosa NetLogo) environments, along with the activities designed for the

study, were planned with the aim of enhancing participants’ CK in science. This included the utilisation of an inquiry-based instructional approach and collaborative learning.

2.5 Summary

Lack of CK in science is a common challenge for primary teachers. Therefore, improving science education in primary schools is a universal concern. Much research has been conducted to enhance the quality of teaching in primary schools where the aim was to improve the quality of primary teachers; in particular, by enhancing their science CK and confidence in their ability in science learning and teaching during their learning as preservice teachers.

It is crucial to help teachers to build and improve their science CK and confidence in their ability in science if they are to add science to the learning areas that they are required to teach and to teach effectively. Improving teacher education programs is one rational way to achieve this. Teacher education programs need to provide more learning opportunities and experiences to boost CK and confidence in ability in science for primary teachers. Thus, understanding how to support the development of preservice teachers’ science CK and confidence in science is an important issue in planning primary teacher education programs.

Many interventions aiming at providing primary teachers with learning opportunities and experiences to boost their CK and confidence in their ability in science have been implemented. These are similar in the features of teaching strategies applied in content and methods courses during teacher education programs that have been recognised as having the potential to enhance primary teachers’ science CK and confidence in ability in science. Research demonstrates that the use of instructional methods that emphasise

inquiry-based methods, PBL, hands-on experience and group work (collaborative) learning, in addition to learning science content and practising teaching, can reform and improve primary teachers’ science CK and confidence in ability in science, and may lead them to implement effective practices in teaching science in their classrooms. All of these approaches to learning are grounded in and supported by constructivist learning theory. This theory advocates that knowledge must be constructed by the learner building on existing experiences and knowledge, and cannot be transmitted (Moore, 2003). According to constructivist theory, learning occurs as learners attempt to make sense of a situation based on what is already known (i.e., prior knowledge) and fit it with their own experience. The effectiveness and success of constructivist approaches in enhancing learners’ CK and confidence has been shown in research discussed in this chapter. Kelly (2000) suggests that a constructivist-based primary science methods course can enhance PK and science knowledge, and increase science teaching self-efficacy. Narayan and Lamp (2010) found that involving preservice primary teachers in a constructivist, inquiry- based science class (inquiry-based pedagogical strategies) is a major factor increasing their self-efficacy.

As the development of knowledge and confidence are complex, this study draw upon many theories that relate to various aspects of preservice teachers learning, understanding and confidence in science, such as constructivism, self-efficacy, active learning, immersivity, situated learning and visualization. In this sense, the study is multifaceted and draws upon several theories to enable the researcher to understand the research questions.

Although considerable effort has been devoted to helping improve primary teachers’ science CK and confidence in their ability in science, there remain serious concerns

relating to this problem in science education in primary schools. Therefore, novel interventions are required. There is a call in the literature to conduct research that addresses the issue of primary teachers’ lack of science CK and lack of confidence in teaching science (Appleton, 2002, 2003; Bayer Corporation, 2004; Bleicher, 2007, 2009; Harlen, 1997; Harlen & Holroyd, 1997; Howitt, 2007; Palmer et al., 2015). Similarly, as few studies have investigated the effect of a combination of immersive and modelling environments on school students’ understanding in science (Jacobson et al., 2016), there is a need to understand how such a combination of platforms can contribute to both knowledge and confidence in science for preservice teachers, as no studies have investigated the combination of immersive and computer modelling environments on preservice primary teachers’ understanding and confidence in science, as undertaken in the current study.

The next chapter describes the methodology used in this study to address the research questions. The chapter presents information concerning the method used in this research, along with a justification for the use of this method. The chapter describes the various stages of the research, including the process used to select participants and allocate groups, the methods used to collect data and the approach used to analyse the data. The chapter also outlines how the learning experience was designed, developed and implemented.