Premium
A L inked D ataset of medical educational resources
Author(s) -
Dietze Stefan,
Taibi Davide,
Yu Hong Qing,
Dovrolis Nikolas
Publication year - 2015
Publication title -
british journal of educational technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.79
H-Index - 95
eISSN - 1467-8535
pISSN - 0007-1013
DOI - 10.1111/bjet.12276
Subject(s) - metadata , computer science , interoperability , world wide web , domain (mathematical analysis) , open educational resources , reusability , interface (matter) , educational resources , cluster analysis , categorization , data science , software , operating system , artificial intelligence , psychology , mathematical analysis , pedagogy , mathematics , bubble , maximum bubble pressure method
Reusable educational resources became increasingly important for enhancing learning and teaching experiences, particularly in the medical domain where resources are particularly expensive to produce. While interoperability across educational resources metadata repositories is yet limited to the heterogeneity of metadata standards and interface mechanisms with a lack of shared or aligned controlled vocabularies, L inked D ata ( LD ) principles, based on W 3 C standards and supported through a wide range of tools, open up opportunities to alleviate such problems. We introduce the “m E ducator L inked E ducational R esources” dataset, which offers a range of open educational resources for the medical domain, exposed through LD principles. Data have been generated through a combination of manual curation and semi‐automated harvesting techniques, and state‐of‐the‐art enrichment and clustering techniques were deployed in order to classify and categorize data, toward improved reusability and access. Data are currently used by a range of educational applications and is accessible for third parties and developers, for instance through the L inked U p C atalog and other registries, to facilitate further take‐up and applications.