Adaptive Educational Games: Providing Non-invasive Personalised Learning Experiences
DIGITEL '08 Proceedings of the 2008 Second IEEE International Conference on Digital Game and Intelligent Toy Enhanced Learning
Adventures in level design: generating missions and spaces for action adventure games
Proceedings of the 2010 Workshop on Procedural Content Generation in Games
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Towards Player Adaptivity in a Serious Game for Conflict Resolution
VS-GAMES '11 Proceedings of the 2011 Third International Conference on Games and Virtual Worlds for Serious Applications
On the harmfulness of secondary game objectives
Proceedings of the 6th International Conference on Foundations of Digital Games
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A case study of expressively constrainable level design automation tools for a puzzle game
Proceedings of the International Conference on the Foundations of Digital Games
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 2013 ACM SIGSAC conference on Computer & communications security
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One of the most promising ways that games for learning can improve education is by adapting to each child individually. However, it is often difficult to instrument game mechanics so that they can be controlled to promote learning. Furthermore, even if this parameterization is possible, there is little knowledge of how to generate adaptive level progressions that optimize engagement and learning. We have taken the first step towards enabling adaptivity in an educational game for teaching fractions through the automatic generation of levels in a way that allows for multiple axes of mathematical and spatial difficulty to be controlled independently. We propose to expand on this work by developing a framework for representing conceptual knowledge. This framework will keep track of each player's knowledge, generate game levels that are tailored to the player's knowledge and skill level, and create progressions of these levels that allow players to learn new concepts through experimentation. We will compare multiple adaptive concept sequencing algorithms by evaluating their effects on player learning and engagement through multivariate tests with tens of thousands of players.