How and Why Learning Simulations Work

pixelfountain design and develop learning simulations, so we thought we’d share some pixelfountain expertise, which is equally applicable to adults as children, rather than our usual games-ED and education focused bloggery.  Not every subject lends itself to a simulation approach and not every learning need or outcome requires such a comprehensive method.  However, learning simulations can be extremely powerful learning tools and this blog article should help explain what learning simulations are and why they are used.

What are Learning Simulations?

First, they are different to games and models, although there is some overlap:

	Games	Learning Simulations	Models
Atmosphere:	Fun	Serious yet fun	Serious
Purpose:	Entertainment	Learning / Knowledge	Information / Knowledge
Accuracy:	Fantasy / Randomness	Real enough	As accurate as can be
Usage:	Open-ended / Flexible	Flexible	Specific
Audience:	Gamers	Learners	Technicians
Usability / User Interface:	Hard in more detailed games	Easy to use	Can be hard
Engagement	Obsessional	Engaging	Boring

Research into Learning Simulations

A 2010 meta-analysis study conducted by researchers from University of Colorado Denver Business School reported that workers trained on simulation games versus formal classroom or web based tutorials, do their jobs better with greater skill and higher retention of relevant information. Workers not only had higher attainment of declarative knowledge and procedural knowledge at 11% and 14%, but more importantly they developed self-efficacy at a significantly higher rate - 20%. This is one’s belief in their own ability and competence to perform effectively in challenging situations with the intrinsic motivation to attain the desired goal or outcome.

Where Can Learning Simulations be Used?

Learning simulations also have various features that match to learning outcomes, but the subject area has to be appropriate:

  

Subject Area	Learning Need / Outcome
Big picture: ·         The subject can be thought of in terms of PESTLE (Political, Economic, Social, Technological, Legal & Environmental). ·         Individuals act according to drivers.	Strategic thinking: ·         Allowing learners to understand how differing PESTLE drivers can impact on direction and requirements. ·         Allowing learners to see issues from other people’s perspective. ·         Providing learners with the skills to make more strategic decisions or understand that decisions have a strategic dimension.
Complexity - components of the system (organisation / community / environment) need to be understood in terms of their relationship to other components.	Systems thinking: ·         Allowing learners to understand and deal with complexity. ·         Allowing learners to think of the machine rather than the cogs within it. ·         Providing learners with a mental map of the system enabling them to make better networks and improve decision making.
Cause and Effect - Decisions can have impacts throughout the system. If this then that and that and that, which makes …	·         Decision making - allowing learners to think about the impact of decisions.  ·         Joined-up thinking - allowing learners to think about multiple variables rather than linear thinking - typical of traditional training. ·         Providing an implementation mind set: this is how things work, this is how I can get things done.
Authenticity (situational understanding): ·         The subject is best understood in context. ·         The conduct of individuals is best understood in context.	Situational / Behavioural Understanding: ·         Allowing learners to test hypotheses and make mistakes. ·         Allowing learners to experience pressure and develop techniques to deal with it. ·         Allowing learners to understand that their decisions and behaviours impact on others and the system. Practice makes perfect. ·         Providing learners with a robust set of skills and behaviours (collaborative mind set). ·         Providing trainers with an integrated approach (testing skills and behaviours).

What Makes Learning Simulations so Powerful?

Sitting behind a simulation is an algorithm. This is a set of equations that “model” a situation. These equations are impacted by variables that change due to decisions made by the learners. What happens in a simulation is dependent on the actions of learners and is not buffeted by the vagaries of the coin toss or a Chance Card. That is not to say that randomness has no place whatsoever in a simulation. For example, when calculating the temperature at a given moment in a climate change simulation might require a degree of randomness (say 20%), which makes the simulation feel more real; i.e. the weather is not totally predictable.

Real Enough

Whilst the simulation “models” a situation, it is not a model in the strictest sense. A learning simulation needs to be real enough to allow learners to quickly explore a situation without getting too bogged down in detail.

Ensuring the Algorithm is Real Enough:
[Italics show input form client and subject matter experts (SME)].
• Analysis & Initial Design:
   o   Scoping with client.
   o   Interviews with subject matter experts.
   o   Initial design created and is signed off.
• Detailed Design:
   o   Look and feel + user interface.
   o   Algorithm developed with input from SME.
   o   Incidents developed.
   o     Detailed design created and is signed off.
• Build:
   o    Media and interface.
   o    Incorporate algorithm.
• Testing & Tuning.
• Piloting:
   o    User group plays the simulation and provides feedback.
   o    The simulation is then tweaked.
• Train the Trainer and roll out of the programme.

We will discuss more about how to develop a learning simulation in a couple of weeks with our very own infographic.

Please follow @paulladley on Twitter, games-ED on Pinterest and like games-ED’s Facebook and Google+ pages for blog updates and interesting games based learning findings.