Abstract
Adaptive content selection is recognized as a challenging research issue in adaptive educational hypermedia systems (AEHS). Several efforts have been reported in literature aiming to support the Adaptation Model (AM) design by providing AEHS designers with either guidance for the direct definition of adaptation rules, or semi-automated mechanisms which generate the AM via the implicit definition of such rules. The goal of the semi-automated, decision-based approaches is to generate a continuous decision function that estimates the desired AEHS response, aiming to overcome the insufficiency and/or inconsistency problems of the defined adaptation rule sets. Although such approaches bare the potential to provide efficient AMs, they still miss a commonly accepted framework for evaluating their performance. In this paper, we propose an evaluation framework suitable for validating the performance decision-based approaches in adaptive learning objects selection in AEHS and demonstrate the use of this framework in the case of our proposed decision-based approach for estimating the desired AEHS response.
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References
Knutov, E., De Bra, P., Pechenizkiy, M.: AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques. New Review of Hypermedia and Multimedia 15(1), 5–38 (2009)
Henze, N., Nejdl, W.: A Logical Characterization of Adaptive Educational Hypermedia. New Review of Hypermedia and Multimedia (NRHM) 10(1), 77–113 (2004)
Karampiperis, P., Sampson, D.G.: Adaptive Learning Resources Sequencing in Educational Hypermedia Systems. Educational Technology & Society 8(4), 128–147 (2005)
Ras, E., Ilin, D.: Using decision models for the adaptive generation of learning spaces. In: Nejdl, W., Kay, J., Pu, P., Herder, E. (eds.) AH 2008. LNCS, vol. 5149, pp. 153–162. Springer, Heidelberg (2008)
Brusilovsky, P., Wade, V., Conlan, O.: From Learning Objects to Adaptive Content Services for E-Learning. In: Architecture Solutions for E-Learning Systems, pp. 243–261. Idea Group Inc, USA (2007)
Karampiperis, P., Sampson, D.G.: Adaptive Learning Object Selection in Intelligent Learning Systems. Journal of Interactive Learning Research, Special Issue on Computational Intelligence 15(4), 389–409 (2004)
Sampson, D., Karampiperis, P.: Decision Models in the Design of Adaptive Educational Hypermedia Systems. In: Graf, S., et al. (eds.) Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers. IGI Global (2011)
Biletskiy, Y., Baghi, H., Keleberda, I., Fleming, M.: An adjustable personalization of search and delivery of learning objects to learners. Expert Systems with Applications 36(5), 9113–9120 (2009)
Dolog, P., Simon, B., Nejdl, W., Klobucar, T.: Personalizing Access to Learning Networks. ACM Transactions on Internet Technology 8(2), p. Article 8 (2008)
Ochoa, X., Duval, E.: Relevance Ranking Metrics for Learning Objects. IEEE Transactions on Learning Technologies 1(1), 34–48 (2008)
Brusilovsky, P.: Adaptive navigation support. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 263–290. Springer, Heidelberg (2007)
Papanikolaou, K., Grigoriadou, M., Kornilakis, H., Magoulas, G.: Personalising the Interaction in a Web-based Educational Hypermedia System: the case of INSPIRE. International Journal of User Modeling and User-Adapted Interaction 13(3), 213–267 (2003)
IEEE. IEEE Learning Object Metadata Standard (IEEE LOM), IEEE P1484.12.1. (2002), http://ltsc.ieee.org/wg12/
Najjar, J., Duval, E.: Actual Use of Learning Objects and Metadata: An Empirical Analysis. IEEE Technical Committee on Digital Libraries Bulletin (TCDL)Â 2(2) (2006)
Honey, P., Mumford, A.: The manual of Learning Styles.: Peter Honey, Maidenhead (1992)
Razmerita, L.: User modeling and personalization. In: Adaptable and Adaptive Hypermedia Systems. Idea Group Inc., USA (2005)
IMS, IMS Global Learning Consortium Inc., Learner Information Package (LIP) Final Specification v1.0 (2001)
McCalla, G.: Smart recommendation for an evolving e-learning system: architecture and experiment. International Journal on E-Learning 4(1), 105–129 (2005)
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Karampiperis, P., Sampson, D.G. (2011). Performance Evaluation of Adaptive Content Selection in AEHS. In: Tsihrintzis, G.A., Virvou, M., Jain, L.C., Howlett, R.J. (eds) Intelligent Interactive Multimedia Systems and Services. Smart Innovation, Systems and Technologies, vol 11. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22158-3_19
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DOI: https://doi.org/10.1007/978-3-642-22158-3_19
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