This thesis describes the development of 'Bayes Academy', an educational game which aims to teach an understanding of Bayesian networks.
A Bayesian network is a directed acyclic graph describing a joint probability distribution function over n random variables, where each node in the graph represents a random variable. To find a way to turn this subject into an interesting game, this work draws on the theoretical background of meaningful play. Among other requirements, actions in the game need to affect the game experience not only on the immediate moment, but also during later points in the game. This is accomplished by structuring the game as a series of minigames where observing the value of a variable consumes 'energy points', a resource whose use the player needs to optimize as the pool of points is shared across individual minigames. The goal of the game is to maximize the amount of 'experience points' earned by minimizing the uncertainty in the networks that are presented to the player, which in turn requires a basic understanding of Bayesian networks.
The game was empirically tested on online volunteers who were asked to fill a survey measuring their understanding of Bayesian networks both before and after playing the game. Players demonstrated an increased understanding of Bayesian networks after playing the game, in a manner that suggested a successful transfer of learning from the game to a more general context. The learning benefits were gained despite the players generally not finding the game particularly fun.
ACM Computing Classification System (CCS):
- Applied computing - Computer games
- Applied computing - Interactive learning environments
- Mathematics of computing - Bayesian networks
