Open Educational Resources (OER) provide a strategic opportunity to improve the quality of education as well as facilitate policy dialog, knowledge sharing, and capacity building. One of the fundamental concepts of OER is “the ability to freely adapt and reuse existing pieces of knowledge.” Reuse of educational resources by both individuals and organizations may have significant creative and economic benefit for the educational environment. This Special Issue editorial introduces six interesting experiences, representative of the use of OER in engineering education in important areas such as the production of open content at various scales, reuse of contents, institutional open Web site initiatives, and technological applications to support the exploitation of OER, all at different degrees of maturity. Since some readers will be unfamiliar with prior work on OER, this Special Issue also outlines the hot topics in OER and the critical factors for success when joining the Open Educational Movement. Finally, the editorial provides a set of recommendations and examples offered by the Special Issue editors from their over 6 years of experience leading a research group in semantic Web technologies applied to Open Education. A key requirement, in their opinion, is to improve the metadata interoperability between various collections of open material, so as to facilitate the discoverability and subsequent combining, remixing, or adapting OER; that is, OER data should be easily accessible to any user.

Purpose: – The aim of this paper is to present an initiative to apply the principles of Linked Data to enhance the search and discovery of OpenCourseWare (OCW) contents created and shared by the universities. Design/methodology/approach: – This paper is a case study of how linked data technologies can be applied for the enhancement of open learning contents. Findings: – Results presented under the umbrella of OCW-Universia consortium, as the integration and access to content from different repositories OCW and the development of a query method to access these data, reveal that linked data would offer a solution to filter and select semantically those open educational contents, and automatically are linked to the linked open data cloud. Originality/value: – The new OCW-Universia integration with linked data adds new features to the initial framework including improved query mechanisms and interoperability.

In an open and distributed platform as the Web there are problems associated with the heterogeneity and information overload. In this context, teaching community and learners may need some support to discover the Open Educational Resources best suited for their learning processes. In this paper, the authors propose the high-level design of a framework for the recommendation of learning content in a flexible way. In order to provide a personalized set of resources, an adaptive approach of filtering is used according to the data available of each user. To achieve this goal, the framework has been designed taking into account the best features provided by technologies of the Semantic Web in order to find online material.

Nowadays, text mining is a long studied field in science, the vast amount of text resources available has made scientist explore several domains through many different techniques. One of the main processes in text mining are cleaning, reduction and transformation before the application of a classification algorithm. Preprocessing has a large impact in classification algorithms because text is an unstructured form of data with very large number of dimensions and therefore it can be seen as a very sparse matrix. These characteristics that make text so complex are addressed by preprocessing algorithms which extract the main data features. We present a work with a comparison of the performance of the different preprocessing algorithms for a classification problem in two datasets written in Spanish and English.