Friday, 29 November 2013

What Can g-Learning Designers Learn from Commercial Games?

First of all, if you don’t know what g-Learning is, have a look at our post “g-Learning: Is this the learning term we have been looking for?”.  Then have a look at the infographic courtesy of and our insight below.

Games' Big Hitters

So, what can we learn from the above infographic?  Does it contain any useful information for g-Learning designers?  We think so.  There are some useful reassurances about perceptions of games and some interesting demographic insight (average game player is 30 years of age).  However, we think that g-Learning designers should take note to the types of games and gameplay that feature on this list as the ‘top’ games.  The top games on the list can be split into several broad genres:

Adventure (e.g. Assassin’s Creed, Halo)
Sport (e.g. FIFA, Madden NFL)
Strategy (e.g. Age of Empires, The Sims)
Party (e.g. Just Dance, Guitar Hero)

This is useful in itself, as g-Learning designers could design their games around these genres/styles.  However, what interests me, is how most of these games are famously played cooperatively.  Obviously you can play most of them solo, people may only ever play them solo and some of them can only really be played solo (e.g. The Sims).  However, by and large, the top games seem to be the ones which allow cooperation and collaboration.

This is interesting, as it suggests yet again that gamers aren’t all anti-social loners and that this view of gaming is seriously outdated (see our article, “Gamer Myths: Infographic and 10 Facts").  However, for g-Learning designers, it suggests that perhaps we should be creating more collaborative learning games.  Perhaps these are the most enjoyable or engaging.  Click here for a case study of one of our collaborative learning games, to see how we do it.  Interestingly, our market for sim-uni is also adults.  Click here to see our “Adult Games Based Learning and sim-uni” post.

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Thursday, 21 November 2013

ICT in Practice - Games Based Learning: Theory and Practice

I recently wrote an article in ICT in Practice magazine - Games Based Learning: Theory and Practice.

I won't spoil the read too much suffice to say that it covers ground that we have blogged about here, but it is well worth a read as it works as a nice summary. The article introduces games based learning, discusses our design and development approach, the underpinning learning theory (situated learning) and goes on to present a case study of the use of our Sustainaville product in schools.

The article has got picked on twitter and other blogs. Ryan Schaaf at the Committed Sardine Blog does a particularly nice rewrite.

A demonstration of Sustainaville can be found at

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Sunday, 17 November 2013

Women and Game Design

This week, we thought we'd share an infographic with you.  One of our fans from alerted us to it.  Hopefully it will be inspirational for some of our readers (and particularly young women, like myself).  If you would like to learn more about easy ways to get into
programming, click here or click here for more about designing a learning simulation.

Women in Design

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Thursday, 7 November 2013

Research into the Brain: Implications for Game Design

Image courtesy of  Frenkieb on Flickr
There has been quite a bit of research into the effects of games on the brain.  For example, a study looking at the effects of playing Mario 64 by researchers from the Max Planck Institute for Human Development had very interesting findings.  The research found that those who played at least 30 minutes of Mario 64 every day for two months grew significant amounts of new grey matter in three areas of the brain.  The areas were correlated with spatial navigation, strategic planning, working memory, and motor performance.  The control group who did not play Mario, didn’t have the same growth (their grey matter actually decreased; attributed to ageing).  For a nice summary of the research, click here.

However, what can games designers learn from research into the brain?

A fantastic article by Ben Lewis-Evans looks at research into dopamine.  He concludes that while the research is interesting and may provide some insights, it is rarely applied and especially not to gaming.  He thinks it would be more useful for games designers to use observational data from test subjects playing their game, than looking to neuroscience for answers.  However he does say: “One exception could be that a theoretical neurological approach may be able to detect if a player was ‘wanting’ to play your game without consciously realising it (something that may indeed be possible)” and “All this said, if you are interested in knowing what your games may be doing to peoples brains, or perhaps you are working in serious games and want to see if games can improve (or worsen) brain function. Then, here, neuroscience can be valuable.”

While this seems negative, it is a powerful insight.  Ben discusses how people both over-use and over-trust the prefix ‘neuro’.  He urges us to be ‘neuroskeptical’ and not think something is more worthwhile just because it sounds scientific and important.  He thinks that while research into the brain may improve and be more useful in the future, at the moment it might be better to stick with observing people’s behaviours.  And what is wrong with that?  Most games designers won’t have access to laboratories and MRI scanners.  So, if games designers can try and use some premises of neuroscience, along with common understandings of people’s behaviours and observations, they should be suitably well informed where the brain is concerned.  And, if researchers do try to see the serious effects of games (as in the example above), that is great and could help to fill in some of the blanks and provide much sought after ‘evidence’ that games can have serious learning outcomes.

Ben also lists some of the findings from non-neuro research into the preferences/motivations etc. of gamers, which provide powerful insight and help for games designers.  Just because these findings weren’t obtained through peoples’ brains being hooked up to a machine, they are no less of an insight into the mind’s inner workings and human behaviour.  For example:

  • Rewards that are unpredictable (loot drops) are generally more motivating than rewards that are predictable (100 xp per monster).
  • Rewards should be meaningful, e.g. food is not particularly motivating for most people if you are already full, or if you are in a relatively visually sparse setting then new, unusual, stimuli will attract your attention more readily.
  • People tend to have a preference for immediate rewards and feedback and are not so motivated by delayed rewards and feedback. This preference for immediate gratification is strongest when young, but persists throughout life.
  • A predictor for a reward can serve/become a replacement for that reward in terms of behavioural response (e.g. getting points in a game becomes associated with having fun and points can therefore become a motivating reward in themselves).

For the full article and Ben’s list of sources, click here.

Image courtesy of Scott M on Flickr
I have referred to cognitive flow before in my article: “10 Resources on Creating your Own Learning Game”.  This is another area of research into the brain through observations, and it has many implications for game design.  Mihaly Csikszentmihalyi found that skill and task difficulty cause people to have different cognitive and emotional reactions to stimuli.  When their skill level is too low and the task is too hard, people become anxious.  However, if the task is too easy and their skill level is too high, people become bored. ‘Cognitive Flow’, like in the story of Goldilocks, is when skill level and difficulty are ‘just right’.

During this state, there are several effects.  For example, extreme focus on the task and the experience of the task itself being the justification for continuing it.  Csikszentmihalyi also defined four characteristics found in tasks likely to induce Flow states.  Tasks were likely to:

  1. Have concrete goals with manageable rules.
  2. Demand actions to achieve goals that fit within the person's capabilities.
  3. Have clear and timely feedback on performance and goal accomplishment.
  4. Diminish extraneous distraction, thus facilitating concentration.

This obviously has huge potential for games designers, and particularly for learning games designers.  A fantastic job of describing Flow in more detail and unpacking its implications for game designers has been done by Gamasutra here.

So, as you can see, observations into human behaviour and mental states can be powerful tools for game designers.  I couldn’t resist tagging this article about neuro-gaming onto the end of this blog post.  It looks at how future gaming technologies could tap into our whole nervous system and drive gameplay (brain wave data, emotional states, emotional recognition, pupil dilation etc.).

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