Personalized Learning Path Based on Metadata Standards

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Colace, F., De Santo, M. & Vento, M. (2005). Personalized Learning Path Based on Metadata Standards. International Journal on E-Learning, 4(3), 317-335. Norfolk, VA: AACE.
Retrieved from http://www.editlib.org/p/4820.

Journal Information

International Journal on E-Learning
ISSN 1537-2456
Volume 4, Issue 3, 2005
Association for the Advancement of Computing in Education (AACE)  Norfolk, VA

More Information on IJEL

Authors

Francesco Colace, Massimo De Santo, Mi Vento, Università degli Studi di Salerno, Italy

Abstract

Thanks to the technological improvements of recent years, distance education represents a real alternative or support to the traditional formative processes. The Internet allows the design of contents, which are able to raise the quality of the traditional formative process. However, the amount of information students can obtain from the Internet is immense and students can easily be confused. Teachers can also be disconcerted by this quantity of content and they are often unable to suggest the correct content to their students. A solution to these problems can be derived from the ever more detailed description of each content area: in literature this process is defined as creating metadata. This approach, in fact, can support the introduction in an e-learning environment of a new software module: the Intelligent Tutoring System. These modules can easily build personalized learning paths. In fact, the real problem is often that of organizing lessons genuinely based on student profiles and not only a simple sequencing of contents. This article proposes a Java and JSP technology-based tool for metadata creation and management and the automatic selection of contents to form a sequencing of lessons and to complete a learning path. With this tool teachers can describe the contents, student profiles and ontology, according to a standard model that at this moment is "standard de facto." In this tool we have integrated a module that from the standard description of various resources (student profiles, content descriptions, etc.) deduces their digest representative vector. By comparing these vectors this module automatically finds the most suitable set of contents for every student profile.

Keywords

• Standards
• Students
• Distance Education
• Tutoring

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