Investigation on Student Modeling in Adaptive E-learning Systems
Muesser Cemal Nat, University of Greenwich, United Kingdom
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Vancouver, Canada ISBN 978-1-880094-76-1 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
Adaptive e-learning systems hold promise for the future development as innovative technologies continuously appear in the field. Along with the facilities that they provide, they have led to enhanced education. Students can receive customized learning with improved alternatives for learning anytime and anywhere. This field has a direct relation with the emergence of new technologies, advances in learning, machine learning and artificial intelligence therefore future of this field is wide open (Shute, 2007). This paper aims to investigate developed and emerging technologies for student modelling in personalized e-learning systems and discuss a proposed style that is being developed to address issues in the field of adaptive e-learning. Various techniques have been generated for collecting data about students’ characteristics and integrated into e-learning systems.
Cemal Nat, M. (2009). Investigation on Student Modeling in Adaptive E-learning Systems. In T. Bastiaens, J. Dron & C. Xin (Eds.), Proceedings of E-Learn 2009--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2420-2427). Vancouver, Canada: Association for the Advancement of Computing in Education (AACE).
© 2009 Association for the Advancement of Computing in Education (AACE)
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