Self-organizing content management with semantic neural networks

Authors:
Harri Ketamo
Affiliations:
Satakunta University of Applied Sciences, Pori, Finland
Venue:
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Year:
2009

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Abstract

Semantic neural networks, as knowledge representations, are relatively extensible and they can be used to model the characteristics of users, patterns of behavior and competencies in order to support the performance of individuals. This study focuses on the design and evaluation of a completely adaptive, semantic neural network -based, educational system. The study focuses on characteristics of Complex Adaptive Systems: self-organization, entropy and emergence. In the empirical evaluation, the systematic organization of progression that emerged from disordered progressions could be called emergence. In terms of user experiences, most of the users recognize that the self-organization was sound and it supported learning, but in several cases the users felt that the system was punishing them too much.