Conference paper
Static and dynamic difficulty level design for edutainment game using artificial neural networks
Springer Berlin
Edutainment 2006: Technologies for E-Learning and Digital Entertainment (Hangzhau, China, 16/04/2006–19/04/2006)
2006
Abstract
When designing a game, one of the major tasks is to design a game of exciting and challenging difficulty levels to maintain the interest level of a player throughout the game. This is especially important when designing an educational game. This paper proposes the use of Artificial Neural Networks (ANNs), specifically the Backpropagation Neural Networks (BPNNs) for handling the gaming experience. The BPNNs can provide targeted learning experience for the user or the student. This will achieve personalized learning that is an important issue for student relationship management. The proposed frameworks will provide motivation for the student as the difficulty level progresses and adjusts to suit individual users.
Details
- Title
- Static and dynamic difficulty level design for edutainment game using artificial neural networks
- Authors/Creators
- K.W. Wong (Author/Creator) - Murdoch UniversityC.C. Fung (Author/Creator) - Murdoch UniversityA. Depickere (Author/Creator) - Murdoch UniversityS. Rai (Author/Creator) - Murdoch University
- Conference
- Edutainment 2006: Technologies for E-Learning and Digital Entertainment (Hangzhau, China, 16/04/2006–19/04/2006)
- Publisher
- Springer Berlin
- Identifiers
- 991005540127907891
- Copyright
- © Springer-Verlag Berlin Heidelberg 2006
- Murdoch Affiliation
- School of Information Technology
- Language
- English
- Resource Type
- Conference paper
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