Adapting serious games using DDA is a way to adjust the difficulty of the game to match the competence of the play-learner. This doesn’t always mean making the game easier but sometimes means the exact opposite. In this post i will discuss the methods, affordances and constraints of using DDA for serious games.
When playing a game, a player becomes frustrated when the game is too difficult. This is not necessarily a bad thing as an important aspect of games is to make players fail to let them master the skill to advance a scenario or a level. However, it is important to control the type of failure which occurs during gameplay. Good games provide appropriate level of challenge (Clifford, 1984) which motivates players to overcome a challenge rather than get frustrated and give up. On the other hand, if the difficulty is too low, the players get bored and disengaged from the gameplay. Due to these factors, it is important to maintain an optimum level of difficulty in the game to keep the player engaged.
However, it is extremely difficult for a designer to create a game which matches the skill level of each player at all times during the game. This is a bigger problem for serious games because they don’t have the leisure of having a self-selecting audience which picks a game based on their skill level. If we want to incorporate serious games in areas like classrooms, healthcare, and military organizations, it is important that we create games which are appropriate for all participants and not a select few. This is where DDA can play an important role.
A common approach to difficulty adjustment in games is to let the player choose the difficulty level at the beginning of the game. In some cases, games provide the option to change difficulty level during gameplay. The problem associated with this type of difficulty adjustment is the ambiguity. How is a player supposed to know what easy, medium or hard means, before playing the game? Even with the option to change difficulty later on, it is difficult to infer what aspects of the game will become more difficult or easier. What if a player is good at shooting enemies but bad at combo moves? Similarly, in serious games, what if while playing a language acquisition game, a player is good at writing, but bad at pronunciations? Should she be made to replay a level just because she’s bad at one aspect of the game? Or should she be provided a level where the focus is to develop her pronunciation while the difficulty of writing is still challenging enough?
A DDA framework, has two core components: player modeling and game content manipulation. Player modeling is the process of inferring relevant skills, competencies and preferences of a player (Nguyen & Do, 2008). A player model refers to a persistent database profile of a player which stores relevant information about a player like their gameplay data, preferences, playing style, skill level in different aspects of the game, etc. This allows the game to understand the player better and hence facilitate appropriate levels of difficulty accordingly.
Game content manipulation is the process of changing elements of the game based on the player model and the game context to adjust the level of difficulty. This can be achieved by adjusting variables like speed (in tetris), number of enemies (in plants vs zombies), gravity (in platformers), etc. using adaptation algorithms. The type of manipulation and the effect it has on the difficulty is dependent on the type of game and on the type of player. This makes the process of manipulation a little tricky, as the designer has to create levels/scenarios in such a way that adaptation of content is possible in real-time. Moreover, it is a lengthy and sometimes even impossible for a designer to pre-calculate all such possibilities and design all the necessary content in advance. A new approach that overcomes this problem and also provides several other affordances is called PCG. I talk more about this method in a dedicated blog post about PCG.
The research on use of DDA in serious games has shown positive results (Soflano, Connolly & Hainey, 2015; Hwang et al., 2012). Even simple adaptive algorithms based on base level player models have achieved these results (Sharek & Wiebe , 2015; Harrison, 2014). In terms of future research, it is necessary to provide further work in this area using sophisticated modeling and algorithms to achieve larger gains in outcomes. With my thesis project i’m trying to address this need by creating an adaptive game for cognitive training.
Image Source: "Girl plays Pac Man" by Lars Frantzen - Own work. Licensed under CC BY-SA 3.0 via Commons
Shashank Pawar 