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:
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Games
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Learning
Simulations
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Models
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Atmosphere:
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Fun
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Serious yet fun
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Serious
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Purpose:
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Entertainment
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Learning / Knowledge
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Information / Knowledge
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Accuracy:
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Fantasy / Randomness
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Real enough
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As accurate as can be
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Usage:
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Open-ended / Flexible
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Flexible
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Specific
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Audience:
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Gamers
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Learners
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Technicians
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Usability / User
Interface:
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Hard in more detailed games
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Easy to use
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Can be hard
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Engagement
|
Obsessional
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Engaging
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Boring
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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
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Big picture:
·
The
subject can be thought of in terms of PESTLE (Political, Economic, Social,
Technological, Legal & Environmental).
·
Individuals
act according to drivers.
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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.
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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.
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