Affordances and constraints of simulations

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1 Simulations

Stephen Pathipati Arokiaswamy, Memorial University of Newfoundland

2 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.

3 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).

4 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.

5 Links

Interactive simulations

Physics simulations

Science simulations


Interactive simulations for science teaching

6 Works Cited

Akinsola, M. and Animasahun, I. (2007). The effect of simulation-games environment on student’s achievement in and attitudes to mathematics in secondary schools. The Turkish Online Journal of Educational Technology, 6 (3), pp. 113--119.

Corter, J., Esche, S., Chassapis, C., Ma, J. and Nickerson, J. (2011). Process and learning outcomes from remotely-operated, simulated, and hands-on student laboratories. Computers & Education, 57 (3), pp. 2054--2067.

Doerr, H., Arleb Ack, J. and O’Neil, A. (2013). Teaching practices and exploratory computer simulations. Computers in the Schools, 30 (1-2), pp. 102--123.

Dunleavy, M., Dede, C. and Mitchell, R. (2009). Affordances and limitations of immersive participatory augmented reality simulations for teaching and learning. Journal of Science Education and Technology, 18 (1), pp. 7--22.

Edward, N. (1997). Computer based simulation of laboratory experiments. British Journal of Educational Technology, 28 (1), pp. 51--63.

Eskrootchi, R. and Oskrochi, G. (2010). A Study of the Efficacy of Project-based Learning Integrated with Computer-based Simulation-STELLA. Educational Technology & Society, 13 (1), pp. 236--245.

Garrett, B. and Callear, D. (2001). Designing intelligent computer-based simulations: a pragmatic approach. Research in Learning Technology, 9 (3).

Hulshof, C. and De Jong, T. (2006). Using just-in-time information to support scientific discovery learning in a computer-based simulation. Interactive Learning Environments, 14 (1), pp. 79--94.

Kiboss, J., Wekesa, E. and Ndirangu, M. (2006). Improving students’ understanding and perception of cell theory in School Biology using a computer-based instruction simulation program. Journal of Educational Multimedia and hypermedia, 15 (4), pp. 397--410.

Lee, Y., Guo, Y. and Ho, H. (2008). Explore effective use of computer simulations for physics education. Journal of Computers in Mathematics and Science Teaching, 27 (4), pp. 443--466.

Liu, H., Su, I. and Others (2011). Learning residential electrical wiring through computer simulation: The impact of computer-based learning environments on student achievement and cognitive load. British Journal of Educational Technology, 42 (4), pp. 598--607.

Lui, M. and Slotta, J. (2013). Immersive simulations for smart classrooms: exploring evolutionary concepts in secondary science. Technology, Pedagogy and Education, (ahead-of-print), pp. 1--24.

Plass, J., Homer, B. and Hayward, E. (2009). Design factors for educationally effective animations and simulations. Journal of Computing in Higher Education, 21 (1), pp. 31--61.

Sauter, M., Uttal, D., Rapp, D., Downing, M. and Jona, K. (2013). Getting real: the authenticity of remote labs and simulations for science learning. Distance Education, 34 (1), pp. 37--47.

Thomas, R. and Milligan, C. (2004). Putting teachers in the loop: tools for creating and customising simulations. Journal of Interactive Media in Education, 2004 (2).