A semantic web service oriented framework for adaptive learning environments

10Citations
Citations of this article
35Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The current state of the art in supporting e-learning objectives is primarily based on providing a learner with learning content by using metadata standards. Due to this approach, several issues have to be taken into account - e. g. limited re-usability across different standards and learning contexts and high development costs. To overcome these issues, this paper describes an innovative semantic web service-oriented framework aimed at changing this data- and metadata-based paradigm to a highly dynamic service-oriented approach. Instead of providing a learner with static data, our approach is based on fulfilling learning objectives based on a dynamic supply of services. Therefore, we introduce a semantic layer architecture to abstract from existing learning data as well as process metadata standards by using Semantic Web Service (SWS) technology. Furthermore, our approach is based on abstract and reusable learning process models describing a learning process semantically as a composition of learning goals. Based on the formal semantic descriptions of learning goals as well as web services, services appropriate to achieve a specific learning goal can be selected, composed and invoked dynamically. This supports a high level of re-usability since a dynamic adaptation to different learning contexts and requirements of individual learners is achieved while utilizing standard-compliant learning applications. To illustrate the application of our approach, we describe a prototypical implementation utilizing the introduced approach based on the SWS framework WSMO. © Springer-Verlag Berlin Heidelberg 2007.

Cite

CITATION STYLE

APA

Dietze, S., Gugliotta, A., & Domingue, J. (2007). A semantic web service oriented framework for adaptive learning environments. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4519 LNCS, pp. 701–715). Springer Verlag. https://doi.org/10.1007/978-3-540-72667-8_49

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free