Computer simulation

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1 Definition

“Computer simulation is defined as having the following two key features:

  1. There is a computer model of a real or theoretical system that contains information on how the system behaves.
  2. Experimentation can take place, i.e. changing the input to the model affects the output.

As a numerical model of a system, presented for a learner to manipulate and explore, simulations can provide a rich learning experience for the student. They can be a powerful resource for teaching: providing access to environments which may otherwise be too dangerous, or impractical due to size or time constraints; and facilitating visualisation of dynamic or complex behaviour.” (Thomas and Milligan, 2004)

See also:

2 Simulation in education

Simulations can be considered a variant of cognitive tools, i.e. they allow students to test hypothesis and more generally "what-if" scenarios. In addition, they can enable learners to ground cognitive understanding of their action in a situation. (Thomas and Milligan, 2004; Laurillard, 1993). In that respect simulations are compatible with a constructivist view of education.

Most authors seem to agree that use of simulations needs to be pedagogically scaffolded. “Research shows that the educational benefits of simulations are not automatically gained and that care must be taken in many aspects of simulation design and presentation. It is not sufficient to provide learners with simulations and expect them to engage with the subject matter and build their own understanding by exploring, devising and testing hypotheses.” (Thomas and Milligan, 2004: 2). The principal caveat of simulations is that students rather engage with the interface than with the underlying model (Davis, 2002). This is also called video gaming effect.

Various methods can be used, e.g.:

  • the simulation itself can provide feedback and guidance in the form of hints
  • Human experts (teachers, coaches, guides), peers or electronic help can provide assistance using the system.
  • Simulation activities can be strongly scaffolded, e.g. by providing built-in mechanisms for hypothesis formulation (e.g. as in guided discovery learning simulation)
  • Simulation activities can be coached by humans

2.1 Types of simulations

(to do)

2.2 Effectiveness

According to Gandolfi, Ferdig & Immel (2018, p.970) [1], “Within K-12, D’Angelo et al. (2013) [2] [in a short literature review] found that simulations can be positively used when learners are interacting with models. This is particularly true when those models are difficult to observe in real life. Merchant et al. (2014) [3] completed a meta-analysis and found that simulations were useful when students were assessed immediately after the instruction. Simulations were also found to be more useful when integrated as practice sessions rather than stand-alone activities. Finally, Sitzmann (2011) [4]discovered that gaming simulations increased post-training self-efficacy, declarative knowledge, procedural knowledge, and retention. The most positive outcomes occurred when students were actively involved, when students could access the simulations whenever they wanted, and when they supplemented learning rather than serving as stand-alone activities.”

Traci Sitzmann (2001) [5] argues that simulation games simultaneously engage affective and cognitive processes, which is more effective according to interactive cognitive complexity theory (Tennyson & Jorczak, 2008) [6]. Results of their meta-analytics study “are favorable regarding the use of simulation games in training. Self‐efficacy, declarative knowledge, procedural knowledge, and retention results all suggest that training outcomes are superior for trainees taught with simulation games relative to the comparison group.” (Sitzmann, 2001)

See also guided discovery learning

2.3 The inquiry learning perspective

Inquiry learning is defined as "an approach to learning that involves a process of exploring the natural or material world, and that leads to asking questions, making discoveries, and rigorously testing those discoveries in the search for new understanding" (National Science Foundation, 2000). This means that students adopt a scientific approach and make their own discoveries; they generate knowledge by activating and restructuring knowledge schemata (Mayer, 2004)). Inquiry learning environments also ask students to take initiative in the learning process and can be offered in a naturally collaborative setting with realistic material.” (De Jong, 2006).

According to the What do we know about computer simulations, common characteristics of educational computer simulations are:

  • Model Based: Simulations are based on a model. This means that the calculations and rules operating the simulation are programmed. These calculations and rules are collectively called "the model", and it determines the behavior of the simulation depending on user actions.
  • Interactive: Learners work interactively with a simulation's model to input information and then observe how the variables in the simulation change, based on this output.
  • Interface driven: The value changes to the influenced variables and the observed value changes in the output are found in the simulation's interface.
  • Scaffolded: Simulations designed for education should have supports or scaffolds to assist students in making the learning experience effective. Step by step directions, or small assignments which break the task down to help students, while they work with a simulation, are examples.

3 Software

  • SimQuest (Note there is also a commercial SimQuest system for BioMedical Simulation)
  • JeLSIM - Java eLearning SIMulations. Jelsim Builder is a tool for the rapid production of interactive simulations (Jelsims). (Dead link:
  • NetLogo and AgentSheets are programmable micorworlds allowing all sorts of agent/cells simulations
  • Some multi-purpose cognitive/classroom tools like Freestyler do have embedded simulations tools.

See also:

  • System dynamics. Software for simulations with differential and difference equations, etc. are indexed there.

4 Links

(needs additions !)

4.1 Introductions and Overviews

4.2 Indexes

  •  ???

4.3 Associations

5 References

Cited with footnotes
  1. Gandolfi, E., Ferdig, R. E., & Immel, Z. (2018). Educational opportunities for augmented reality. In Voogt, J., Knezek, G., Christensen, R., & Lai, K. W. (Eds.). Second Handbook of Information Technology in Primary and Secondary Education (pp. 968-977). Springer.
  3. Merchant, Z., et al. (2014). Effectiveness of virtual reality-based instruction on students’ learning outcomes in PreK-12 and higher education: A meta-analysis. Computers & Education, 70, 29–40.
  4. Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64, 489–528.
  5. Sitzmann, T. (2011). A meta-analytic examination of the instructional effectiveness of computer-based simulation games. Personnel Psychology, 64(2), 489–528.
  6. Tennyson RD, Jorczak RL. (2008). A conceptual framework for the empirical study of instructional games. In O’Neil HF, Perez RS (Eds.), Computer games and team and individual learning (pp. 39–54). Oxford , UK : Elsevier.
  • Kaleidoscope Network of Excellence for Technology Enhanced Learning (2007). What do we know about computer simulations ?, PDF (based on a Dutch brochure written by Ton de Jong and Wouter van Joolingen).
  • Davies, C., H., J. (2002). "Student engagement with simulations." Computers and Education 39: 271-282.
  • De Jong, Ton (2006) Computer Simulations: Technological Advances in Inquiry Learning, Science 28 April 2006 312: 532-533 DOI: 10.1126/science.1127750
  • De Jong, T. (2006b). Scaffolds for computer simulation based scientific discovery learning. In J. Elen & R. E. Clark (Eds.), Dealing with complexity in learning environments (pp. 107-128). London: Elsevier Science Publishers.
  • de Jong, Ton; van Joolingen, Wouter R. (1998). Scientific Discovery Learning with Computer Simulations of Conceptual Domains, Review of Educational Research, Vol. 68, pp. 179-201.
  • Gijlers, H. (2005). Confrontation and co-construction; exploring and supporting collaborative scientific discovery learning with computer simulations. University of Twente, Enschede.
  • David Guralnick, Christine Levy, Putting the Education into Educational Simulations: Pedagogical Structures, Guidance and Feedback, International Journal of Advanced Corporate Learning (iJAC), Vol 2, No 1 (2009) Abstract/PDF (Open access journal).
  • Hickey, D. T., & Zuiker, S. (2003). A new perspective for evaluating innovative science learning environments. Science Education, 87, 539-563.
  • Jackson, S., Stratford, S., Krajcik, J., & Soloway, E. (1996). Making dynamic modeling accessible to pre-college science students. Interactive Learning Environments, 4, 233-257.
  • Ketelhut, D. J., Dede, C., Clarke, J., & Soloway, E. (1996). A multiuser virtual environment for building higher order inquiry skills in science. Paper presented at the American Educational Research Association, San Francisco.
  • Lee, J. (1999). "Effectiveness of computer-based instructional simulation: a meta analysis." International Journal of Instructional Media 26(1): 71-85
  • Laurillard, D. (1993). Rethinking University Education: a framework for effective use of educational technology, Routledge.
  • Mayer, R. E. (2004), Should there be a three strikes rule against pure discovery? The case for guided methods of instruction. Am. Psych. 59 (14).
  • National Science Foundation, in Foundations: Inquiry: Thoughts, Views, and Strategies for the K-5 Classroom (NSF, Arlington, VA, 2000), vol. 2, pp. 1-5 HTML.
  • Parush, A., Hamm, H. & Shtub, A. (2002). "Learning histories in simulation-based teaching: the effects on self learning and transfer." Computers and Education 39: 319-332.
  • Reigeluth, C. & Schwartz, E. (1989). "An instructional theory for the design of computer-based simulation." Journal of Computer-Based Instruction 16(1): 1-10.
  • Swaak, J. (1998). What-if: Discovery simulations and assessment of intuitive knowledge. Unpublished PhD, University of Twente, Enschede.
  • Swaak, J., Van Joolingen, W. R., & De Jong, T. (1998). Supporting simulation-based learning; the effects of model progression and assignments on definitional and intuitive knowledge. Learning and Instructions, 8, 235-253.
  • Thomas, R.C. and Milligan, C.D. (2004). Putting Teachers in the Loop: Tools for Creating and Customising Simulations. Journal of Interactive Media in Education (Designing and Developing for the Disciplines Special Issue), 2004 (15). ISSN:1365-893X
  • Van Joolingen, W. R., & De Jong, T. (1991). Characteristics of simulations for instructional settings. Education & Computing, 6, 241-262.
  • Van Joolingen, W. R., & De Jong, T. (2003). Simquest: Authoring educational simulations. In T. Murray, S. Blessing & S. Ainsworth (Eds.), Authoring tools for advanced technology educational software: Toward cost-effective production of adaptive, interactive, and intelligent educational software (pp. 1-31). Dordrecht: Kluwer Academic Publishers.
  • Van Joolingen, W. R., De Jong, T., Lazonder, A. W., Savelsbergh, E. R., & Manlove, S. (2005). Co-lab: Research and development of an online learning environment for collaborative scientic discovery learning. Computers in Human Behavior, 21, 671-688.
  • Van Joolingen, W.R. and King, S. and Jong de, T. (1997) The SimQuest authoring system for simulation-based discovery learning. In: B. du Boulay & R. Mizoguchi (Eds.), Artificial intelligence and education: Knowledge and media in learning systems. IOS Press, Amsterdam, pp. 79-86. PDF
  • White, B., & Frederiksen, J. (1998). Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16, 3-118.