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This paper aims to provide an overview of the issues related to developing a digital repository for learning objects. Given the unique nature of digital learning objects, compared to other types of digital materials, several factors must be considered when establishing a learning object repository.

The conclusions of the paper are drawn from a review of the current state of the learning object repository market.

The issues related to creating digital learning object repositories are, in some cases, significantly different from those in creating “traditional” digital repositories. Staffing, metadata, and use considerations must be carefully considered in this unique environment.

This paper fills a gap in the current literature by providing a general overview of the current state of software and related practices in the learning object repository area.

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.

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.

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 paper is to describe an academic library’s successful implementation of a WordPress-based learning object repository (LOR) that facilitates widespread sharing of learning objects, allowing librarians to save prep time and to scale up the library instruction program. Both practical and technical implications of using WordPress software as a repository platform are discussed.

WordPress is free, open-source software that may be used as a platform for an LOR. By using WordPress software to create a public index of content hosted on a server, the library created an LOR that features a record for each learning object which contains metadata about the object’s format, content and accessibility. The WordPress platform/repository is also expandable, through additional free and paid plug-ins, to function as a simple learning management system that may also issue badges.

As a result of implementing an LOR, librarians save time prepping for classes by reusing or adapting their colleagues’ work, librarians are able to offer tutorials to online students and the repository also serves as a showcase for the library instruction program.

Many academic libraries are feeling increased pressure to scale up library instruction and serve more online students. This paper illustrates a case study of how one library uses an easy-to-implement and low-budget LOR to make progress toward these goals.

The purpose of this article is to describe the rapidly changing learning landscapes academic libraries are facing using a case study of Arizona State University. Flexible, adaptable and low‐cost solutions are available for libraries who find themselves in a diverse learning landscape coupled with economic challenges.

This case study provides details of the processes for implementing low‐cost alternatives to creating, managing and disseminating learning objects.

Low‐cost solutions can give libraries an important information literacy presence within the university learning landscape. Locally managed learning object repositories can be implemented with minimal fuss and facilitate faculty and student in a wide range of academic programs access to library learning content.

How to manage learning content in a local learning objects repository to maximize access, use and reuse for faculty, students and librarians. This article describes an option for implementing a learning object repository with few fiscal and personnel resources.

Online information literacy on a budget is within reach for many academic libraries by tapping into the potential of free or low‐cost web resources for creating and managing learning content.

This paper aims to explore the feasibility of using web‐mining technology on learning object (LO) usage information to discover the LO relation pattern and provide valuable recommendations on related learning resources. Design/methodology/approach – This paper proposes three kinds of learning object relation patterns and gives a specific definition of each pattern based on analysing the learners' usage data stored in the learning object repository. These relation patterns can be used to make effective recommendations to learners.

LO usage data indicate the potential relation patterns between LOs. By using web‐mining technology on the usage data, it is possible to discover valuable relation patterns.

The authors propose a set of LO relation patterns and indicate how they are closely related to users' learning behaviour.

Research in the area of technology‐enhanced learning (TEL) throughout the last decade has largely focused on sharing and reusing educational resources and data. This effort has led to a fragmented landscape of competing metadata schemas, or interface mechanisms. More recently, semantic technologies were taken into account to improve interoperability. The linked data approach has emerged as the de facto standard for sharing data on the web. To this end, it is obvious that the application of linked data principles offers a large potential to solve interoperability issues in the field of TEL. This paper aims to address this issue.

In this paper, approaches are surveyed that are aimed towards a vision of linked education, i.e. education which exploits educational web data. It particularly considers the exploitation of the wealth of already existing TEL data on the web by allowing its exposure as linked data and by taking into account automated enrichment and interlinking techniques to provide rich and well‐interlinked data for the educational domain.

So far web‐scale integration of educational resources is not facilitated, mainly due to the lack of take‐up of shared principles, datasets and schemas. However, linked data principles increasingly are recognized by the TEL community. The paper provides a structured assessment and classification of existing challenges and approaches, serving as potential guideline for researchers and practitioners in the field.

Being one of the first comprehensive surveys on the topic of linked data for education, the paper has the potential to become a widely recognized reference publication in the area.

The purpose of this research is to propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries.

The paper presents experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment examines raw server log data generated from the interactions of the students with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects and the learning process.

Preliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish relationships between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means.

During the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also seek information outside the virtual campus. All these usage data are not considered in the model proposed here. Further research is needed in order to obtain a complete view of the seeking information behavior of students for improving the users' profile and creating better personalized services.

This paper suggests how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community.

From libraries beginnings, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source.

This paper aims to describe continuing effort to develop a repository of teaching materials for sharing and reuse in library and information science (LIS) schools in Asia. It also aims to propose a framework for carrying out a user study to validate the taxonomy and metadata, and evaluate how they support the reuse of teaching materials in four Asian countries.

This study uses a combination of approaches. The first phase focuses on building a repository of learning materials powered by taxonomy and metadata with enhanced searching and browsing capabilities. The second phase uses different evaluation techniques to propose a framework for assessing the usability of learning materials.

The potential for reuse of teaching materials in the area of information organization is examined by analyzing how various aspects of this topic are covered in three Master's programs in Information Studies, Knowledge Management, and Information Systems at Nanyang Technological University, Singapore. A faceted taxonomy has been helpful in tagging different types of learning objects. A metadata scheme based on DC‐Education and LOM has been used for enhanced description of learning materials.

This paper makes contributions in several ways. It suggests guidelines for developing taxonomies in different domains; describes steps in building repositories of learning materials; and suggests a methodology for studying reusability of learning material.