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.