Treffer: Static and dynamic difficulty level design for edutainment game using artificial neural networks
Title:
Static and dynamic difficulty level design for edutainment game using artificial neural networks
Authors:
Source:
Technologies for E-Learning and Digital Entertainment (First international conference, Edutainment 2006, Hangzhou, China, April 16-19, 2006)Lecture notes in computer science. :463-472
Publisher Information:
Berlin: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 11 ref 1
Original Material:
INIST-CNRS
Subject Terms:
Education, Éducation, Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Distraction, Distracción, Divertissement, Entertainment, Education, Educación, Jeu ordinateur, Computer games, Jeu vidéo, Video game, Videojuego, Personnalisation, Customization, Personalización, Réseau neuronal, Neural network, Red neuronal, Téléenseignement, Remote teaching, Teleensenanza
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
School of Information Technology, Murdoch University, South St, Murdoch, Western Australia 6150, Australia
Division of Arts, Murdoch University, South St, Murdoch, Western Australia 6150, Australia
Division of Arts, Murdoch University, South St, Murdoch, Western Australia 6150, Australia
ISSN:
0302-9743
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.19152313
Database:
PASCAL Archive
Weitere Informationen
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.