Automatic adaptive retrieval of learning objects

Abstract

This paper aims to propose a new approach to automatic retrieval of Learning Objects (LOs) in an adaptive e-Learning using multidimensional learner characteristics to enhance learning effectiveness. The approach focuses on adaptive techniques in three components of e-Learning: Learning Paths, LO Retrieval, and LO Sequencing Levels. This approach has been designed to enable the adaptation of rules which are represented by Prolog to become generic. Hence, the application to various domains is possible. The approach dynamically selects, and sequences LOs into an individual learning package based on the use of domain ontology, learner profiles, and LO metadata. The ontologies are represented by Web Ontology Language (OWL). The Sharable Content Object Reference Model (SCORM) is employed to represent LO metadata and learning packages in order to support LO sharing. The IMS Learner Information Package Specification (IMS LIP) is used to represent learner profiles. Both standards are represented by means of Extensible Markup Language (XML). Thus, the information can be exchangeable and interoperable with other systems. Based on the proposed approach, a prototype system has been developed and evaluated. It has been discovered that the system has yielded positive effects in terms of the learners’ satisfaction.