Analysing and improving embedded markup of learning resources on the web

Dietze, Stefan, Taibi, Davide, Yu, Ran, Barker, Phil, & d'Aquin, Mathieu. (2017). Analysing and Improving Embedded Markup of Learning Resources on the Web. Paper presented at the Proceedings of the 26th International Conference on World Wide Web Companion, Perth, Australia.

Abstract

Web-scale reuse and interoperability of learning resources have been major concerns for the technology-enhanced learning community. While work in this area traditionally focused on learning resource metadata, provided through learning resource repositories, the recent emergence of structured entity markup on the Web through standards such as RDFa and Microdata and initiatives such as schema.org, has provided new forms of entitycentric knowledge, which is so far under-investigated and hardly exploited. The Learning Resource Metadata Initiative (LRMI) provides a vocabulary for annotating learning resources through schema.org terms. Although recent studies have shown markup adoption by approximately 30% of all Web pages, understanding of the scope, distribution and quality of learning resources markup is limited. We provide the first public corpus of LRMI extracted from a representative Web crawl together with an analysis of LRMI adoption on the Web, with the goal to inform data consumers as well as future vocabulary refinements through a thorough understanding of the use as well as misuse of LRMI vocabulary terms. While errors and schema misuse are frequent, we also discuss a set of simple heuristics which significantly improve the accuracy of markup, a prerequisite for reusing learning resource metadata sourced from markup.

Publisher

ACM

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

Data Science Institute (Conference Papers)