Adapting a quality assurance framework for creating educational metadata in an agricultural learning repository

Abstract

PurposeThe paper seeks to introduce a process for assuring the creation of quality educational metadata based on the ISO/IEC 19796‐1 standard to describe the agricultural learning resources in the repository of the Consultative Group on International Agricultural Research (CGIAR).Design/methodology/approachThe paper describes the general notion of quality in education and in the creation of educational metadata. It introduces a quality framework based on the ISO/IEC 19796‐1 standard on quality management and quality assurance for learning, education and training. This standard consists of a reference framework for the description of quality approaches (RFDQ) to describe, compare, and analyze quality management and quality assurance approaches, which has been adapted to the creation of educational metadata in the context of the learning object repository of the CGIAR.FindingsIn order to achieve consistency in the description of learning resources in a repository through quality educational metadata, a standardized process for metadata creators is essential. The reference framework of the ISO/IEC 19796‐1 standard provides a flexible approach that allows the optimization of the metadata creation process while assuring quality of the descriptive information.Practical implicationsThe paper proposes a standardized process for the creation of learning object metadata based on the ISO/IEC 19796‐1 standard, and makes suggestions on how to use the reference framework when adapting a quality model for educational metadata.Originality/valueISO/IEC 19796‐1 is a very recent standard with a flexible reference framework to develop a quality model in education and training. It provides a novel approach for organizations maintaining learning repositories that are interested in standardizing the educational metadata creation process, especially when multiple stakeholders are involved.

Metadata quality in digital repositories: Empirical results from the cross‐domain transfer of a quality assurance process

“…Overall, the present study took a first step toward validating a metadata quality assurance process that was originally applied to a federation of learning repositories, for cultural and institutional collections. It provided tangible results in contrast to previous efforts that lacked quantitative results (Stvilia et al., ; Zschocke & Beniest, ) and it extended previous studies that proposed the need for continuous QA processes within the lifecycle of a repository (Poll, ; Vinagre, Pinto, & Ochoa, ). Finally, this study took into consideration a series of recommendations related to the support of metadata annotators with guidelines that were emphasized in past work (Kastens et al., ; Lefoe et al., ; Babalhavaeji et al., ; Saarti et al., ).…”

“…Finally, in the educational domain, Zschocke and Beniest () analyzed different quality metrics for metadata and proposed a quality assurance framework that can be applied in the metadata creation process in the case of an agricultural learning repository. The framework proposed, although not tested using actual data, is really similar to Metadata Quality Assurance Certification Process (MQACP) in the sense that it also uses peer‐review methods to assess the metadata coming from content providers as well as automated methods for the assessment of metadata records.…”

Metadata for Big Data: A preliminary investigation of metadata quality issues in research data repositories

“…The main issues were that the elements should be populated and used correctly, while precise instructions, descriptions and rules should be set. In addition to general metadata standards, like Dublin Core, researchers have examined metadata quality in the context of specialized repositories, such as architectural repositories [15]; digital libraries [20]; agricultural collections [23]; health databases [16]; and learning object repositories [14]. Despite the heterogeneity of metadata and repositories examined in these studies, there is a common set of metadata issues that appears to influence quality.…”