Details
| Original language | English |
|---|---|
| Title of host publication | Linking Theory and Practice of Digital Libraries |
| Subtitle of host publication | 28th International Conference on Theory and Practice of Digital Libraries, TPDL 2024, Proceedings |
| Editors | Apostolos Antonacopoulos, Annika Hinze, Nicholas Vanderschantz, Benjamin Piwowarski, Mickaël Coustaty, Giorgio Maria Di Nunzio, Francesco Gelati |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 85-94 |
| Number of pages | 10 |
| ISBN (electronic) | 978-3-031-72440-4 |
| ISBN (print) | 9783031724398 |
| Publication status | Published - 25 Sept 2024 |
| Event | 28th International Conference on Theory and Practice of Digital Libraries, TPDL 2024 - Ljubljana, Slovenia Duration: 24 Sept 2024 → 27 Sept 2024 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 15178 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Tools for discovering learning resources become increasingly important as more and more educational offerings move online. Improvements in the retrieval or recommendation of these resources often rely on the availability of metadata. For example, it has been demonstrated that showing teachers educational metadata alongside the search result could improve search outcomes. However, this relies on relevant educational metadata being embedded in web pages using formats such as JSON-LD or MicroData. For learning resources, the Learning Resource Metadata Initiative (LRMI) ontology defines classes and properties to express such embedded educational metadata. Previous studies have assessed its adoption, quality, and conformance to the ontology prior to an LRMI version update in 2020. This contribution updates prior studies with respect to adoption following the version change. We then focus on mining usage patterns of LRMI properties for the benefit of application developers who would like to leverage this resource. We also expand our analysis beyond just resources that use LRMI by training a text classifier to identify educational web pages. As a result, we are able to present what we believe to be the broadest and most recent examination of usage patterns and adoption of learning resource metadata on the web.
Keywords
- educational search, learning resource description, metadata quality
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
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Linking Theory and Practice of Digital Libraries : 28th International Conference on Theory and Practice of Digital Libraries, TPDL 2024, Proceedings. ed. / Apostolos Antonacopoulos; Annika Hinze; Nicholas Vanderschantz; Benjamin Piwowarski; Mickaël Coustaty; Giorgio Maria Di Nunzio; Francesco Gelati. Springer Science and Business Media Deutschland GmbH, 2024. p. 85-94 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15178 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - An Updated Analysis of Learning Resource Metadata Usage on the Web
AU - Sebastian, Ratan
AU - Hoppe, Anett
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/9/25
Y1 - 2024/9/25
N2 - Tools for discovering learning resources become increasingly important as more and more educational offerings move online. Improvements in the retrieval or recommendation of these resources often rely on the availability of metadata. For example, it has been demonstrated that showing teachers educational metadata alongside the search result could improve search outcomes. However, this relies on relevant educational metadata being embedded in web pages using formats such as JSON-LD or MicroData. For learning resources, the Learning Resource Metadata Initiative (LRMI) ontology defines classes and properties to express such embedded educational metadata. Previous studies have assessed its adoption, quality, and conformance to the ontology prior to an LRMI version update in 2020. This contribution updates prior studies with respect to adoption following the version change. We then focus on mining usage patterns of LRMI properties for the benefit of application developers who would like to leverage this resource. We also expand our analysis beyond just resources that use LRMI by training a text classifier to identify educational web pages. As a result, we are able to present what we believe to be the broadest and most recent examination of usage patterns and adoption of learning resource metadata on the web.
AB - Tools for discovering learning resources become increasingly important as more and more educational offerings move online. Improvements in the retrieval or recommendation of these resources often rely on the availability of metadata. For example, it has been demonstrated that showing teachers educational metadata alongside the search result could improve search outcomes. However, this relies on relevant educational metadata being embedded in web pages using formats such as JSON-LD or MicroData. For learning resources, the Learning Resource Metadata Initiative (LRMI) ontology defines classes and properties to express such embedded educational metadata. Previous studies have assessed its adoption, quality, and conformance to the ontology prior to an LRMI version update in 2020. This contribution updates prior studies with respect to adoption following the version change. We then focus on mining usage patterns of LRMI properties for the benefit of application developers who would like to leverage this resource. We also expand our analysis beyond just resources that use LRMI by training a text classifier to identify educational web pages. As a result, we are able to present what we believe to be the broadest and most recent examination of usage patterns and adoption of learning resource metadata on the web.
KW - educational search
KW - learning resource description
KW - metadata quality
UR - http://www.scopus.com/inward/record.url?scp=85206220592&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-72440-4_8
DO - 10.1007/978-3-031-72440-4_8
M3 - Conference contribution
AN - SCOPUS:85206220592
SN - 9783031724398
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 94
BT - Linking Theory and Practice of Digital Libraries
A2 - Antonacopoulos, Apostolos
A2 - Hinze, Annika
A2 - Vanderschantz, Nicholas
A2 - Piwowarski, Benjamin
A2 - Coustaty, Mickaël
A2 - Di Nunzio, Giorgio Maria
A2 - Gelati, Francesco
PB - Springer Science and Business Media Deutschland GmbH
T2 - 28th International Conference on Theory and Practice of Digital Libraries, TPDL 2024
Y2 - 24 September 2024 through 27 September 2024
ER -