Adapted from Jesse Schell's 'The Art of Game Design'
Most importantly, a learning game — or gamified learning experience — has to achieve its learning objectives. But to do that it needs to work well as a game, and that means having game balance.
Jesse Schell, in his seminal book, ‘The Art of Game Design’, talks about many types of game balance. Here’s a summary of the key types, refocused with a view to learning games, that you can use to review your own learning game designs.
Balance between players
Players don’t want to feel that things are unfair: that the odds are stacked against them to an unreasonable extent. Opponents shouldn’t have an easier time of things, or be more powerful. The easiest way to achieve this is by giving everyone the same starting position and options.
But differences can be more interesting, and more importantly for learning games, can reflect life better. Maybe you want one player to play the workers, another the management, another the unions. In this case you need to make sure that advantages and disadvantages balance out. If one is faster, how strong should the other be? As always, playtesting is the key to your answer.
Balance between too much and not enough challenge
Too much challenge equals frustration, but too little equals boredom. This is complicated by the fact that players generally get better over time, so what was challenging at the start may not stay that way.
You can balance this by gradually raising the difficulty level as the game progresses. But you can also give ‘grades’ or points for success — then it’s not just about whether players succeed, but how well. Another option is to make things tougher for the leader and easier for those behind. A ‘catch-up’ mechanism like the Blue Shells in Mario Kart can achieve this, or a system that allows those behind to gang up on the leader.
Rewards and punishments can also be tools here. Giving the right kinds of rewards at the right rate makes players feel like their efforts earned a fair payout.
Balance between choices and strategies
If every option is essentially the same, no choice is meaningful. But if one strategy is inherently better than others — a dominant strategy — it devalues all other choices. Similarly to balance between players, this often means balancing out one attribute of choice A with the right amount of a different attribute in choice B.
A common way to do this is with risk and reward — a choice that offers a bigger payout should come with a higher cost or a chance of a bad outcome. Options with matched risk and reward might include:
- Something that gives a great payout but requires more skill
- A chance to go for a greater payout, with the risk of getting nothing if you fail
- A chance to grab something useful, but with a danger before you get there
- A route that’s quicker, but with more dangers
- A route that’s slower, but gives you more assets to use later
Balance between skill and chance
Games of pure skill, like chess, will mean that a more skilful player will almost always win. But where’s the fun in playing if you realise you’ll almost certainly lose? Chance can help with this. But too much chance, and you may as well play the lottery.
If you balance elements of both, players are rewarded for what they do well, but always have surprises and a chance to ‘beat the odds’. Which card you draw is chance, but how you play it is skill. As with every one of these balances, playtesting is key to get this right.
Balance between mental and physical
In a purely mental game, making the right choices is the whole game — you will never make a good choice but fail on execution. When you introduce a physical element of dexterity, speed or strength, execution will always vary. The right balance will depend on the game and the players — for some games, physicality is inappropriate — but some physical element, like chance, can add fun and surprise.
Jenga is a great example of mental and physical balance: you have to choose the right block to move. But you also need a steady hand to make the play.
Balance between competition and cooperation
Many games default to competition by design. But cooperation is also a human instinct, and may be more suitable for workplace learning games. Do you want to teach your people how to win? Or how to work as a team?
An interesting way forward is to balance the two, or give options for both. Should players be able to choose between the glory of a solo win or the advantages or mutual altruism? Options like this can make for rich debriefs and learning.
Balance between too short and too long
Games that are too short don’t provide meaningful opportunities to execute strategies and learn from them. Many learning games have the opposite problem: like Monopoly, the point is well-made some time before the game is done.
This can be difficult to balance because most games need a fixed end point that’s known in advance, and then the players will take as long to get there as they take. But you could also try to:
- use actual time as a game mechanic, with a ‘ticking clock’
- introduce events that accelerate the end of the game or lower the bar for victory
- push players into more conflict as time goes on, so that an end is more likely
Balance between too much and not enough freedom
Too many choices exhaust the player, and are hard to design for. But too few means the game doesn’t feel like a game. This is another balance that’s very situational, but the key lies in thinking about whether fewer choices would make the game clearer, versus whether more choices would make the players feel more empowered.
As well as, of course, in playtesting.
Balance between too simple and too complex
Simple can mean easy and boring, or it can mean elegant. Complex can mean fussy and complicated, or it can mean rich in detail. All of which makes this a very difficult kind of balance: we want our game to be elegant and not boring, rich and not complicated.
The difference is the difference between innate and emergent complexity. Innate complexity is when we add lots of detail consciously. Its symptoms are often long sets of rules and lots of exceptions to rules. With emergent complexity, we keep the rules simple, but they allow for complex and rich situations.