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Static and dynamic difficulty level design for edutainment game using artificial neural networks

Wong, K.W., Fung, C.C., Depickere, A. and Rai, S. (2006) Static and dynamic difficulty level design for edutainment game using artificial neural networks. In: Edutainment 2006: Technologies for E-Learning and Digital Entertainment, 16-19 April 2006, Hangzhau, China.

Link to Published Version: http://dx.doi.org/10.1007/11736639_58
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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.

Publication Type: Conference Paper
Murdoch Affiliation: School of Information Technology
Publisher: Springer Berlin
Copyright: (c) Springer-Verlag Berlin Heidelberg 2006
URI: http://researchrepository.murdoch.edu.au/id/eprint/648
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