Understanding the Optimal Learning Curves in Games

No matter what game we play there is always an element of learning involved. Every action a player takes in game, from input controls to complex combinations and tactics requires learning to take place. Whilst this is true on most cases of unique games we must recognise that many players will have outside skills acquired from other games or activities that make learning that particular game's skill set that much easier for them. Regardless of the pace of learning (irrespective of time) there must be an understanding of what challenges should be faced at what points.

When we delve into the world of 'Flow' we find optimal states represented by an area that balances high skill requirements with high challenges. However to reach that state we must assume the player has no skill to begin with and progress to that point. The following is a diagram re-created for my Dissertation.

Whilst this explains the optimal state, the game must reach that higher end. This is where the learning curve comes into play. Most games will have a simple model, that is when a player is assumed to have more skill, the challenge is increased relative to its growth. The result is a straight line drawn from start to finish. Note that the curve ends not when the game is complete, but when the most difficult challenge is faced, with (ideally) the peak skill level of a player.

The green area represents the traditional flow model, where challenges and skill are roughly equal the subject is motivated. Now this may be the case in many games, however it is not the optimal learning curve. The following is what could be perceived as optimal.

Optimal in this scenario refers to the most enjoyment from a game. This would be achieved through initial learning in a safe environment, where the player is free to test and be tested on their basic skills without dire consequences. Learning during this time is increasing, however the challenges compared are relatively low, gaining momentum ready for the next section. The player feel empowered by defeating these initial challenges with relative ease.

The player is consistently tested throughout the main game scenarios, with rapidly increasing difficulty, but still within the confines of their potential ability. The curve begins to slow at more-or-less over the half way mark of skill. This means that the challenges begin to become part of a rhythm and flow, which is what we as humans enjoy: repetition. If you don't believe me on the repetition thing, think how many times you have watched your favourite film or TV series. The curve towards its final destination comes to an almost stop, as we reach the players perceived peak skill level.

The final section, end-game if you will, challenges everything that the player has learnt throughout, culminating all the skills in one final and most difficult challenge. This is only little more difficult than what the player has already faced, but bring in new twists to push the player to their very limit. More often than not this will be the end of the game, however as mentioned before this curve does not relate to time, should the game continue past this point, it should remain at the same position on the graph.

And there you have it. The optimal learning curve, tailored to meet the needs of players, to empower them, to challenge them, for the most enjoyable experience.