Content Personalization for Inclusive Education through Model-Driven Engineering
Content personalization of e-learning resources has the opportunity to encourage self-directed learning and collaborative activities between students with varying cultures and backgrounds. In the case of students with disabilities, it also has the potential to provide equality of access to learning resources that can be presented in formats that are compatible with a student’s needs and preferences. In this paper, a framework is presented for doing this type of content personalization for students with disabilities using Model-Driven Engineering tools and techniques.
KeywordsUnify Modeling Language User Preference Digital Medium Blended Learning Content Personalization
Unable to display preview. Download preview PDF.
- 1.Baldiris, S., Santos, O.C., Baldiris, S., Barrera, C., Boticario, J.G., Velez, J., Fabregat, R.: Integration of educational specifications and standards to support adaptive learning scenarios in ADAPTAPlan. International Journal of Computer Science and Applications (IJCSA). Special Issue on New Trends on AI techniques for Educational Technologies 5, 1 (2008)Google Scholar
- 5.Epsilon project web site (2009), http://www.eclipse.org/gmt/epsilon
- 6.Fink, J., Kobsa, A., Schreck, J.: Personalized hypermedia information provision through adaptive and adaptable system features: User modelling, privacy and security issues. In: IS&N 1997: Proceedings of the Fourth International Conference on Intelligence and Services in Networks, London, UK, pp. 459–467. Springer, Heidelberg (1997)Google Scholar
- 7.IMS Global Learning Consortium, IMS AccessForAll Metadata Specification, http://www.imsglobal.org/accessibility/ (retrieved, 10/2008)
- 8.Kolovos, D.S., Paige, R.F., Polack, F.A.C.: Model Comparison: a Foundation for Model Composition and Model Transformation Testing. In: Proc. First International Workshop on Global Integrated Model Management (G@MMA) 2006, co-located with ICSE 2006, Shanghai, China, (May 2006)Google Scholar
- 9.Khribi, M., Jemni, M., Nasraoui, O.: Toward a Hybrid Recommender System for E-Learning Personalization Based on Web Usage Mining Techniques and Information Retrieval. In: Richards, G. (ed.) Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 6136–6145. AACE, Chesapeake (2007)Google Scholar
- 12.Velasco, C.A., Mohamad, Y., Gilman, A.S., Viorres, N., Vlachogiannis, E., Arnellos, A., Darzentas, J.S.: Universal access to information services—the need for user information and its relationship to device profiles. In: Universal Access in the Information Society. Springer, Heidelberg (2003)Google Scholar