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.

Constraints

A major constraint in using simulations is their effect on cognitive domain. Liu and Su, (2011) found that simulations loaded with multimedia features like audio, video and different formats of information increase the cognitive workload thereby affecting learning. Lee et al., (2007) concluded that computer based simulations produced different cognitive impact on learners depending on their prior knowledge and gender (Lee et al., 2007) making them unsuitable for some learners. Furthermore, the students were overwhelmed and confused by the excessive information processing while working with simulations (Dunleavy et al., 2009). Simulations cannot provide prompts and feedback to learners of different characteristics (Lee et al., 2008). Simulations lack the presence of real objects and Corter et al., (2011) indicated that “remote and simulated labs may allow the students to “see” or remotely operate the apparatus, but some students may need to touch and interact with the apparatus personally to understand at a deep level what is going on” (p.2064). They further suggested that these labs lack social component as most students work individually while collecting data. On realism Edward, (1997) argued that simulations lack hands on experience no matter how sophisticated the display model is and further questioned that the “flight simulation also can be realistic but who would want to fly with the pilot who had never previously been in the air”? (p.55). Simulations lack realism (Garrett & Callear, 2001) and Eskrootchi and Oskrotchi, (2010) concluded that often they fail due to their complex nature and difficulty in understanding. As data are derived from computers in the simulation labs, participants did not feel the experiment realistic and the data authentic while performing a radioactive simulation (Sauter et al., 2013). They also reported that students felt less beneficial to explore the data as it was generated through simulations than real. In a simulated environment only small amount of visual information is available for the retina to be processed, as a result objects compete at the neuronal level for processing resulting in incomplete awareness (Plass et al., 2009). Simulations cannot solely engage a learner, according to Doerr et al., (2013), the teacher had to prompt and help students to interpret and give meaning to the mathematical representations in the computer simulation environment.