Affordances and constraints of simulations

Simulations

Stephen Pathipati Arokiaswamy, Memorial University of Newfoundland

Definitions and background

Wekesa et al., 2006 defined computer-based instruction simulation (CBIS) as an instructional technique that combines animated color graphics to present the dynamic nature of the process through a multi-sensory approach. Doerr et al., (2013) suggested there is a need to design activities in order to motivate students to make sense of meaningful situations. Corter et al., (2011) argued that laboratory experiences need to change since technology and economic trends have transformed educational institutions and curriculum. On simulations Sauter et al., (2013) indicated that they do not have real devices but simulate data from computer models. Thomas and Milligan, (2004) explained that simulation is a synonym of animation and defined simulation as “ a computer model of a real or theoretical system that contains information on how the system behaves and experimentation can take place ,changing the input to the model affects the output” (p.3). Simulations according to Hulshof and Jong, (2006) provide virtual learning environments where learners can design perform experiments and manipulate variables. According to Akinsola and Animahasun, (2007) learning has to be experiential, motivational and engaging. They suggested that simulation game is an antidote that can facilitate experiential learning.

Affordances

         A significant affordance of computer simulations is the time factor. Lee et al., (2008) found that simulations are capable of providing virtual experiments and are generally least time consuming than the real hands - on laboratory experiments. On time factor Liu and Su (2011) indicated that through computer simulations, learning tasks can be conducted multiple times without having to manually conduct the experiment each time. The variables can be changed in simulations, and according to Eskrootchi and Oskrochi (2010), “simulations offer an easy way of controlling experimental variables, opening up the possibility of exploration and hypothesizing” (p.239). Thomas and Milligan (2004) also posited that when exploration is more constrained, visualisations through simulations allow the learners to investigate the effect of changing the variables. Doerr et al., (2013) found that simulations in physics helped students to explore the mathematical model of the relationship between various variables through editable graphs and the drag and drop interface giving more flexibility to express their ideas and to manipulate the variables.
        Sauter et al., (2013) indicated that computers are capable of regulating the settings and measurements of the device minimising the human errors than hands-on labs. They also suggested that computer based labs are valuable for schools with limited resources where lack of equipment leads to less engagement and poor preparation for the future science courses. Additionally Garrett and Callear (2001) described that simulations in clinical settings are realistic than paper - based activities as they facilitate individualized advice and heuristic learning instead of lecture based factual learning. Edward (1997) posited that experiments involving small molecules and large power stations can be simulated as practical real object models cannot be made.
        According to Liu and Su (2011) certain experiments such as electrical wiring systems in traditional labs are time consuming resulting in limited learning, whereas more simulated experiments can be performed in less time and moreover electrical simulations are safe. Investigations involving slow biological and rapid electronic processes can be conveniently studied through simulations at convenient time pace (Edward, 2007). Plass et al., (2009) reported that students remembered the weather maps better when presented through simulations rather than paper maps. To conclude, the affordances of augmented reality are real world environments, ability to communicate with the team members on multiple dimensions and kinaesthetic learning (Dunleavy et al., 2009).