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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.

This paper aims to propose a set of quantitative statistical indicators for measuring the scientific relevance of research groups and researchers, based on high-impact open-access digital production repositories.

An action research (AR) methodology is proposed in which research is associated with the practice; research informs practice and practice is responsible for informing research in a cooperative way. AR is divided into five phases, beginning with the definition of the problematic scenario and an analysis of the state of the art and ending with conducting tests and publishing the results.

The proposed indicators were used to characterise group and individual output in a major public university in south-eastern Mexico. University campuses hosting a large number of high-impact research groups. These indicators were very useful in generating information that confirmed specific assumptions about the scientific production of the university.

The data used here were retrieved from Scopus and open access national repository of Mexico. It would be possible to use other data sources to calculate these indicators.

The system used to implement the proposed indicators is independent of any particular technological tool and is based on standards for metadata description and exchange, thus facilitating the easy integration of new elements for evaluation.

Many organisations evaluate researchers according to specific criteria, one of which is the prestige of journals. Although the guidelines differ between evaluation bodies, relevance is measured based on elements that can be adapted and where some have greater weight than others, including the prestige of the journal, the degree of collaboration with other researchers and individual production, etc. The proposed indicators can be used by various entities to evaluate researchers and research groups. Each country has its own organisations that are responsible for evaluation, using various criteria based on the impact of the publications.

The proposed indicators assess based on the importance of the types of publications and the degree of collaborations. However, they can be adapted to other similar scenarios.

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.