This article investigates a scenario of reuse in which existing learning resources serve as preliminary products for the creation of new learning resources. Authors should be able to reuse learning resources and also parts of them at different levels of granularity in a modular way. The requirements of multigranularity reuse are analyzed and compared to existing solutions. A concept for modular, multigranularity reuse is presented in this article. It is also shown how this kind of reuse can be achieved in practise.

Multigranularity reuse of learning resources

The reusability in learning objects has always been a hot issue. However, we believe that current approaches to e-Learning failed to find a satisfying answer to this concern. This paper presents an approach that enables capitalization of existing learning resources by first creating "content metadatardquo through text mining and natural language processing and second by creating dynamically learning knowledge objects, i.e., active, adaptable, reusable, and independent learning objects. The proposed model also suggests integrating explicitly instructional theories in an on-the-fly composition process of learning objects. Semantic Web technologies are used to satisfy such an objective by creating an ontology-based organizational memory able to act as a knowledge base for multiple training environments.

Enhancing Learning Objects with an Ontology-Based Memory

The definition of 'learning object' is still subject to debate and controversy, causing some degree of ambiguity. Proposed definitions differ in terms of broadness of scope, focus on reusability and requirements on metadata, among other characteristics. Nonetheless, if we approach the definition from the narrower perspective of engineering software systems that deal with learning objects, they can be characterised by the scenarios they are capable of engaging in or by the services for which they provide support. This connects the concept of metadata quality with a situation-specific definition of learning objects. Consequently, a resource may be considered - from the automation perspective - as a valid learning object in a given scenario, while in others it should be discarded due to the lack of certain characteristics. Since automated processes and Web services can be unambiguously specified, this definition enables the specification of conformance profiles with precise and specific requirements on learning objects.

Scenario-oriented reusable learning object characterisations

Knowledge management is characterized by many different activities rang- ing from the elicitation of knowledge to its storing, sharing, maintenance, usage and creation. Skill management is one of such activities, with its own peculiarities, as it focuses on full exploitation of knowledge individuals in an organization have, in order to carry out at best given tasks. In this paper a semantic-based automated Skill Man- agement System is proposed, which supports competences search and creation. The system implements an approach exploiting the formalism and the reasoning services provided by Description Logics. The approach embeds also non standard Description Logics reasoning services to extend the set of provided features. Here we present main characteristics of our system and focus on a novel algorithm exploiting advanced in- ference services for the one-to-one assignment of a set of individuals to a set of tasks, endowed of logical explanation features for missing/conflicting skills.

/pdf/semantic-based-skill-management-for-automated-task-4ovfv168at.pdf

Semantic-based Skill Management for Automated Task Assignment and Courseware Composition

Automating competence management through non-standard reasoning

When machine-understandability is required to build software modules that automatically retrieve and combine learning objects, learning object relationships should be carefully considered, as they raise important semantic issues that influence runtime behaviour. In this paper, we analyse how learning object relationships have an effect on learning object contracts and look for analogies with the object-oriented paradigm. Being some of the most common relationships, we focus on the commitments that aggregation and generalization impose on learning object contracts.

On the semantics of aggregation and generalization in learning object contracts

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On the semantics of aggregation and generalization in learning object contracts