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The purpose of this paper is to describe the provision of existing learning object repositories with enhanced forms of describing digital resources on organic agriculture and agroecology.

The approach followed was to investigate non‐invasive techniques for semantic annotation of learning objects, for which use was made of two tools. The first tool was a software application for the automated classification of learning resources stored in public learning object repositories. The second tool was an ontology in OWL derived from the knowledge in the Agrovoc thesaurus.

Current digital repositories for educational resources and open access archives provide scholars with a number of features, such as the ability to search for materials according to given criteria, or to retrieve the full content of those materials from the repository. Many provide advanced features as well, such as browsing, assessing and collaboratively peer reviewing learning resources, but at the cost of using the specific tools and interfaces provided by each repository.

This research is part of the EU‐funded project Organic.Edunet, aimed at facilitating access, usage and exploitation of digital educational content related to organic agriculture and agroecology (OA&AE). Consequently, knowledge representation, thesauri and the educational resources reported herein are deliberately focused on the OA&AE domain, even though the concepts and techniques utilized may be easily applied in other contexts.

The paper describes two new approaches aimed at enhancing availability of learning resources: the potential use of ontologies for the description of learning resources, and the full use of the classification category in the IEEE LOM metadata standard.

The paper, aims to introduce an educational content management system Hypermedia Learning Objects System (hylOs), which is fully compliant to the IEEE LOM eLearning object metadata standard. Enabled through an advanced authoring toolset, hylOs allows the definition of instructional overlays of a given eLearning object mesh.

In educational content management, simple file distribution is considered insufficient. Instead, IEEE LOM standardised eLearning objects have been well established as the basic building blocks for educational online content. They are nicely suited for self‐explorative learning approaches within adaptive hypermedia applications. Even though eLearning objects typically reside within content repositories, they may propagate metadata relations beyond repository limits. Given the explicit meaning of these interobject references, a semantic net of content strings can be knotted, overlaying the repository infrastructure.

Based on a newly introduced ontological evaluation layer, meaningful overlay relations between knowledge objects are shown to derive autonomously. A technology framework to extend the resulting semantic nets beyond repository limits is also presented.

This paper provides proof of concept for the derivation and use of semantic content networks in educational hypermedia. It thereby opens up new directions for future eLearning technologies and pedagogical adoption.

The paper illustrates capabilities of the hylOs eLearning content management. The hylOs is built upon the more general Media Information Repository (MIR) and the MIR Adaptive Context Linking Environment (MIRaCLE): its linking extension. MIR is an open system supporting the standard XML, CORBA and JNDI. hylOs benefits from manageable information structures, sophisticated access logic and high‐level authoring tools like the eLO editor responsible for the semi‐manual creation of meta data and WYSIWYG like XML–content editing, allowing for rapid distributed content development.

Over the last few years, networking technologies and distributed information systems have moved up the OSI layer and are established well within application‐centric middleware. Most recently, content overlay networks have matured, incorporating the semantics of data files into their self‐organisational structure with the aim of optimising data‐centric distributed indexing and retrieval. This paper elaborates a corresponding concept of semantic structuring for educational content objects. It introduces and analyses the autonomous generation and educational exploitation of semantic content nets, providing proof of concept by a full‐featured implementation within the hylOs educational content management system.

This paper aims to show the results obtained from the data mining techniques application to learning objects (LO) metadata.

A general review of the literature was carried out. The authors gathered and pre‐processed the data, and then analyzed the results of data mining techniques applied upon the LO metadata.

It is possible to extract new knowledge based on learning objects stored in repositories. For example it is possible to identify distinctive features and group learning objects according to them. Semantic relationships can also be found among the attributes that describe learning objects.

In the first section, four test repositories are included for case study. In the second section, the analysis is focused on the most complete repository from the pedagogical point of view.

Many publications report results of analysis on repositories mainly focused on the number, evolution and growth of the learning objects. But, there is a shortage of research using data mining techniques oriented to extract new semantic knowledge based on learning objects metadata.

Presently, searching the internet for learning material relevant to ones own interest continues to be a time‐consuming task. Systems that can suggest learning material (learning objects) to a learner would reduce time spent searching for material, and enable the learner to spend more time for actual learning. The purpose of this paper is to present a system of “hybrid search and delivery of learning objects to learners”.

This paper presents a system of “hybrid search and delivery of learning objects to learners” that combines the use of WordNet for semantic query expansion and an approach to personalized learning object delivery by suggesting relevant learning objects based on attributes specified in the learner's profile. The learning objects are related to the learner's attributes using the IEEE LOM and IMS LIP standards. The system includes a web crawler to collect learning objects from existing learning object repositories, such as NEEDS or SMETE.

The presented HSDLO system has the ability to accurately search and deliver learning objects of interest to a learner as well as adjust the learner's profile over time by evaluating the learner's preferences implicitly through the learning object selections.

Since real LOM's from SMETE are not much populated, the system is tested with a limited set of attributes. The system is evaluated using a test bench rather than real learners.

The paper proposes a combination of three search techniques in one system as well an architectural solution which can be used for other types of online search engines.

The MIT libraries were called upon to recommend a metadata scheme for the resources contained in MIT’s OpenCourseWare (OCW) project. The resources in OCW needed descriptive, structural, and technical metadata. The SCORM standard, which uses IEEE Learning Object Metadata for its descriptive standard, was selected for its focus on educational objects. However, it was clear that the Libraries would need to recommend how the standard would be applied and adapted to accommodate needs that were not addressed in the standard’s specifications. The newly formed MIT Libraries Metadata Unit adapted established practices from AACR2 and MARC traditions when facing situations in which there were no precedents to follow.

This paper will describe the way in which the USMARC cataloging schema is used at the Eisenhower National Clearing‐house (ENC). Discussion will include how ENC MARC extensions were developed for cataloging mathematics and science curriculum resources, and how the ENC workflow is integrated into the cataloging interface. The discussion will conclude with a historical look at the in‐house data transfer from ENC MARC to the current production of IEEE LOM XML encoding for record sharing and OAI compliance, required under the NSDL project guidelines.

This paper aims to present and evaluate a web‐based tool, namely ASK‐CDM‐ECTS, which facilitates authoring and publishing on the web descriptions of (open) academic courses in machine‐readable format using an application profile of the Course Description Metadata (CDM) specification, namely CDM‐ECTS.

The paper examines existing open academic courses initiatives and presents a conceptual model for describing an academic course. Next, the elements of the conceptual model are mapped to the CDM specification, as well as to the CDM‐ECTS application profile for describing (open) academic courses with ECTS compatible information. Finally, the ASK‐CDM‐ECTS tool is presented, which allows authoring of (open) academic courses descriptions based on the proposed conceptual model and by following the CDM‐ECTS application profile.

Based on the evaluation results, it is evident that ASK‐CDM‐ECTS can clearly support the process of describing academic courses offered by European Higher Education Institutions (HEIs) with ECTS compatible information following the CDM‐ECTS application profile; and the process of searching and retrieving academic courses offered by European HEIs.

The evaluation scenarios were executed with users’ representatives from only one HEI. Future work intends to conduct further experiments involving users’ representatives from different HEIs for evaluating the proposed tool.

OpenCourseWare has become a social movement aiming to equal opportunities in education. This paper presents the ASK‐CDM‐ECTS tool, which can be exploited for setting‐up European Open Courses Initiatives that will facilitate open access to education and learning for the societies of the EU Member States.

Despite the development of the CDM specification for describing (open) academic courses, and of its application profile CDM‐ECTS, it appears that there exists no software tool that allows authoring and publishing on the web descriptions of (open) academic courses following CDM‐ECTS. Thus, in this paper, the authors address this issue by introducing the ASK‐CDM‐ECTS tool, aiming at describing (open) academic courses following the CDM‐ECTS application profile.

This paper aims to address the need to ensure the quality of metadata records describing learning resources. We propose improvements to a metadata-quality model, specifically for the compliance sub-feature of the functionality feature. Compliance is defined as adherence level of the learning object metadata content to the metadata standard used for its specification. The paper proposes metrics to assess the compliance, which are applied to a set of learning objects, showing their applicability and usefulness in activities related to resources management.

The methodology considers a first stage of metrics refinement to obtain the indicator of the sub-feature compliance. The next stage is the proposal evaluation, where it is determined if metrics can be used as a conformity indicator of learning object metadata with a standard (metadata compliance). The usefulness of this indicator in the information retrieval area is approached through an assessment of learning objects where the quality level of its metadata and the ranking in which they are retrieved by a repository are correlated.

This study confirmed that the best results for metrics of standardization, completeness, congruence, coherence, correctness and understandability, which determine the compliance indicator, were obtained for learning objects whose metadata were better labelled. Moreover, it was found that the learning objects with the highest level of compliance indicator have better positions in the ranking when a repository retrieves them through an exact search based on metadata.

In this study, only a sub-feature of the quality model is detailed, specifically the compliance of learning object standard. Another limitation was the size of the learning objects set used in the experiment.

This proposal is independent from any metadata standard and can be applied to improve processes associated with the management of learning objects in a repository-like retrieval and recommendation.

The originality and value of this proposal are related to quality of learning object metadata considered from a holistic point of view through six metrics. These metrics quantify both technical and pedagogical aspects through automatic evaluation and supported by experts. In addition, the applicability of the indicator in recovery systems is shown, by example to be incorporated as an additional criterion in the learning object ranking.

The purpose of this research is to enable dynamic customization of metadata that describes educational resources in digital repositories.

Users need to describe educational resources in digital repositories according to a user-specific metadata set. As users generally do not have the skills to customize the software application manually, this approach relies on the techniques of model-driven software engineering, which should allow customization of the software application programmatically with no need to develop or order a new software application. An experiment was conducted to evaluate the proposed solution.

A software platform for managing educational resources described by dynamically extendable metadata is proposed. The platform enables the creation of data models that are programmatically transformed to a Web application for the management of educational resources. In this way, users can create their own models of metadata that are relevant in a particular domain.

The solution has been verified by users with technical knowledge. The appropriateness of the model should still be explored for domain experts with little technical knowledge who desire to define new metadata in their domain.

The solution can be used for digital repositories that store diverse educational resources. Each resource could be described using metadata that relates to the domain the resource belongs to.

Digital repositories standardly describe educational resources using some general metadata, which are more focused on the physical characteristics of resources rather than their semantics. The proposed solution introduces custom domain-specific semantics into the description of the resources, which improves their retrieval.

The need for designing the next generation of web service‐based educational systems with the ability of integrating components from different tools and platforms is now recognised as the major challenge in advanced learning technologies. In this paper, we discuss this issue and we present the conceptual design of such environment, referred to as ASK Learning Service Platform (ASK‐LSP), that can support reusing and repurposing of learning scenarios, activities and content in a consistent manner.