Game Mechanics and Learning Theory

By carlton / 9 February 2012

To bring together the series on how learning theories overlap with games, I’ve drawn up a table of how game mechanics relate to the ideas about how we learn.

By using and combining various definitions of game mechanics (Wikipedia, SCVNGR & Gamification.org), it is possible to map how dynamics correspond to the various learning theories. This is not an exact science but does suggest which mechanics can be used to encourage particular ways of learning.

Of course the risk with any sort of exercise like this, is that it becomes formulaic and is wrongly perceived as a rule for creating “learning” games. I don’t believe that is the case. Every game needs to be looked on a special case: as soon as you try to bottle the essence of play, it tends to evaporate.

Mechanic	Definition	Behaviourist	Cognitivist	Constructionist	Experiential	Social
Achievements	Achievements are a virtual or physical representation of having accomplished something.
Action points	Action points limit or control which actions a player performs each turn.
Appointments	Appointment dynamic requires the player to perform some action at a predetermined time or place.
Auction or bidding	An auction or bidding system encourages players to make competitive bids in order to win some prize.
Behavioural Momentum	Behavioural Momentum is the tendency of players to keep doing what they have been doing.
Bonuses/ modifiers	Bonuses are a reward after having completed a series of challenges or core functions.
Capture/Eliminate	Players must capture or eliminate their opponent’s tokens.
Cards	Cards can act as a randomiser to affect game conditions or as tokens to track game states.
Cascading Information Theory	The theory that information should be released in the minimum possible snippets to gain the appropriate level of understanding at each point during a game narrative.
Catch-up	Catch up is a device that makes success more difficult the closer a player gets to it.
Challenge	Challenges have a time limit or competition.
Collaboration	The game dynamic wherein an entire community is rallied to work together to solve a riddle, a problem or a challenge.
Combos	Combos are used often in games to reward skill through doing a combination of things.
Countdown	The dynamic in which players are only given a certain amount of time to do something.
Dice/ Lottery	Randomisers that determine the outcome of an interaction in a game.
Discovery	Also called Exploration, players love to discover something, to be surprised.
Goals	Goals are conditions of victory or success.
Levels	Levels are a system, or “ramp”, by which players are rewarded an increasing value for an accumulation of points.
Loss avoidance/ aversion	Players have to avoid losing tokens, points or position.
Movement	The controlled movement of tokens.
Penalties	The negative consequence of some behaviour or action.
Piece elimination	Whereby the winner captures or destroys the other players’ pieces.
Progression	A dynamic in which success is granularly displayed and measured through the process of completing itemized tasks.
Puzzle guessing	The player who successfully guesses or deduces the answer to a puzzle wins the game
Quests	Quests are a journey of obstacles a player must overcome.
Races	The goal of achieving a certain position first
Resource management/ ownership	The management of game resources including tokens money and points.
Reward (or chain) Schedules	The timeframe and delivery mechanisms through which rewards (points, prizes, level ups) are delivered.
Risk and reward	Risk and reward offers players extra benefits for optional actions.
Role-playing	Role-playing determines the effectiveness of in game actions depending on how authentically the player acts out the role of a fictional character.
Status	The rank or level of a player. Players are motivated by trying to reach a higher level or status.
Structure building	The goal of acquiring and assembling a set of game resources into a predefined structure or one that is better than that of the other players.
Territory control	The goal of controlling the most area on playing surface.
Tile-laying	Tile laying involves players laying down objects in order to gather points or affect the game world.
Toys/ endless play	Games that do not have an explicit end.
Turns	Turns allow players to act or respond in sequence

4 thoughts on “Game Mechanics and Learning Theory”

Abercrombie
28 February 2012 at 9:59 am

this is not an exact science but does suggest which mechanics can be used to encourage particular ways of learning.
carlton
18 July 2012 at 9:38 am

I think so. There definitely seems evidence that by making the activity more engaging, the mechanics improve impact. It doesn’t work in all circumstances but it’s a useful factor to consider.

Thanks for your comment!
ririn dwi agustin
31 December 2012 at 7:30 am

would you like to give me the reason or analysis about why a game mechanic have been related with certain learning theory? Or maybe variables to do the analysis. thanks
carlton
5 January 2013 at 12:58 pm

Hi there,

Thanks for the comment. I’ve had a similar conversation about this piece over on LinkedIn, so I’ll bring that discussion over here! Hope it helps.

Original comment:
“Can you please elaborate on how you decided whether or not a game mechanic aligned well with a particular learning theory.

For example:

You don’t say that Appointment is constructivist but you do say that Movement is.

I ask because without knowing your criteria, it seems easy to think of ways to invalidate the categorisation.

E.g. For Appointment != constructivist – I can imagine a game where you have to grow some bacterial samples or whatever, where it is necessary to make your readings at particular times or you may end up with bad readings.

The appointment mechanic (or perhaps design pattern?) would seem to be appropriate here. It would allow the player to either turn up or NOT turn up, and she would either get the correct reading or an incorrect one – thus constructing her knowledge about the importance of being timely in this context. I.e in Farmville type games, the Appointment mechanic is heavily used – and the way it plays out is that there is a penalty for not responding timely. If that type of time based event was relevant to your subject domain, I imagine Appointment as a design patter would be both ideal generally, and specifically – very constructivist in the way it would support learning.

And on the other side – for Movement == constructivist – I’m unclear how the ability to move tokens around maps at all to constructivist theories. Surely only where the *specifics* of the movement in the game directly relate to the domain knowledge we wish the player to construct.

I.e. if your game is about gravity, or efficiency of driving routes etc – then of course allowing the player to see how movement does not meet their expectations (and thus, must result in the creation of new knowledge) would be constructivist.

But generally, simply allowing the player to move tokens doesn’t seem to necessarily, on its own relate at all to constructivism.”

My response:
“Thanks for the comment. I don’t think it comes across as overly negative at all – the table is a high-level summary of lots of thinking and I don’t claim that its perfect – I’ve tried to interpret the game mechanics in the most appropriate way but I’m sure some could be rearranged.

I’ll try to respond to the two issues you raise although I appreciate you may not agree with my reasoning.

I related Appointment to Behaviourist theory because it is a conditional binary event – you either make it (in which case you avoid some punishment or are rewarded in some way) or you miss it (in which case you are punished or fail to achieve some reward). I didn’t classify it as constructivist because it is not an iterative phenomena.

On the other hand, Movement in and off itself is not an event that can be judged right or wrong, it is the context that creates that condition. “Controlled” movement (as per the definition) is step by step where one action builds on the previous.

As I say, I don’t claim that my effort is perfect by any means, so I’m grateful for the challenge. Hope my responses are helpful!”

And the response to my answer:
“Thanks for your response. I think I understand your view a bit better now.

I like both of your counter examples and can see how one might follow such a train of thought to creating your table.

The problem I have is that it really isn’t hard for each and every instance (probably, I haven’t considered them all!) to think of a counter example that either shows an example where the mechanic is not used in the spirit of the learning theory it is ‘ticked’ with, and similarly, an example where the mechanic is really appropriate for a learning theory it’s not ‘ticked’ with.

I appreciate that you mention it isn’t perfect, and of course, you were more thinking out loud than presenting your “answer to everything”. You’ve certainly got me thinking, so thanks for that! – but I’m thinking that it probably isn’t possible to actually do this kind of an exercise and actually be accurate. At least at this level of granularity.

I.e – we may be able to say “One can use the appointment mechanic thus to support X learning theory; or like this to support Y learning theory” – so what matters is the context and the particular specifics of implementation, rather than the mechanic in its abstract sense.

What I mean is, it’s all about HOW you use the mechanic, rather than WHICH mechanic you use.”

Hope that helps. As I say, I don’t claim that this model is water-tight so welcome the chance to improve it.

Thanks again,
Carlton

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