Preference-based decision making for personalised access to Learning Resources

被引:0
作者
Department of Special Education, University of Thessaly, Argonafton and Filellinon Street, Volos, GR 38221, Greece [1 ]
不详 [2 ]
不详 [3 ]
机构
[1] Department of Special Education, University of Thessaly, Volos, GR 38221, Argonafton and Filellinon Street
[2] Department of Information and Communications Systems Engineering, University of the Aegean, Karlovassi
[3] Department of Computer and Tele Communications Engineering, University of Western Macedonia, Parko Agiou Dimitriou
来源
Int. J. Auton. Adapt. Commun. Syst. | 2008年 / 3卷 / 356-369期
关键词
Learning Resources; Learning technologies specifications and standards; LRs; Personalised access; Preference-based decision making;
D O I
10.1504/IJAACS.2008.019810
中图分类号
学科分类号
摘要
This article addresses the selection of Learning Resources (LRs; learning material, learning activities, etc.) through a preference-based decision-making framework. We consider the case where a set of LRs are maintained within a digital repository and are described in a common format (e.g. through learning technologies specifications and standards). We present a preference-based decision-making framework for selecting among these LRs according to the profile of each individual learner, thus facilitating personalised access to LRs. We argue that the proposed framework overcomes some of the problems caused by the rule-based approaches which are usually employed to facilitate adaptation and personalisation, in general. Copyright © 2008 Inderscience Enterprises Ltd.
引用
收藏
页码:356 / 369
页数:13
相关论文
共 23 条
[1]  
Brusilovsky P., Karagiannidis C., Sampson D., Layered evaluation of adaptive learning systems, International Journal of Continuing Engineering Education and Life-Long Learning, 14, 4-5, pp. 402-421, (2004)
[2]  
Calvi L., Christea A., Towards generic adaptive systems: Analysis of a case study, The Proceedings of the 2nd International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems (AH 02), (2002)
[3]  
Frankola K., Why online learners dropout, Workforce, 10, pp. 53-63, (2001)
[4]  
Henze N., Nejdl W., A logical characterization of adaptive educational hypermedia, New Review of Hypermedia and Multimedia, 10, pp. 77-113, (2004)
[5]  
Horvitz E., Breese J., Heckerman D., Hovel D., Rommelse K., The lumiere project: Bayesian user modeling for inferring the goals and needs of software users, The Proceedings of the 14th International Conference on Uncertainty in Artificial Intelligence (UAI 98), (1998)
[6]  
IMS Meta-data Best Practice Guide for IEEE 1484.12. 1-2002 Standard for Learning Object Metadata, Version 1.3 Final Specification, (2006)
[7]  
Karampiperis P., Sampson D., Adaptive learning objects selection in intelligent learning systems, The Proceedings of the 4th Hellenic Conference on ICT in Education, (2004)
[8]  
Karampiperis P., Sampson D., Adaptive learning resources sequencing in educational hypermedia systems, Educational Technology and Society, 8, pp. 66-82, (2005)
[9]  
Karampiperis P., Sampson D., Automatic learning object selection and sequencing in web-based intelligent learning systems, Web-Based Intelligent E-Learning Systems: Technologies and Applications, pp. 56-71, (2005)
[10]  
Knolmayer G.F., Decision support models for composing and navigating through e-learning objects, The Proceedings of the 36th IEE Annual Hawaii International Conference on System Science, (2003)