Friday, June 30
2:10-2:30 PM
UTC
Damselfish

Quality Metrics for Learning Object Metadata

Full Paper: Conceptual & Empirical Study ID: 16265
  1. aaa
    Xavier Ochoa
    ESPOL
  2. aaa
    Erik Duval
    Katholieke Universiteit Leuven

Abstract: The quality of the learning objects metadata records stored in a repository is important its operation and interoperability. While several studies have tried to define and measure quality for metadata, a scalable and effective way to assess this quality is not currently available. This work converts the fuzzy quality definitions found in those studies into implementable measures (metrics). Several of these metrics are proposed. They are based on the same quality parameters used for human review of metadata: completeness, accuracy, provenance, conformance to expectations, logical consistency and coherence, timeliness, and accessibility. The information requirements to calculate the proposed metrics are also detailed. Some of these metrics are implemented and tested over to two collection of Learning Object Metadata, one of mainly human generated metadata, the other generated by automated means. Early results suggest that the metrics are indeed sensible to quality features in the metadata. Finally, this work recommends further work to validate and calibrate the proposed metrics.

Presider: Ming-Yen Chen, Institute of Manufacturing Engineering, National Cheng Kung University

Topic

Conference attendees are able to comment on papers, view the full text and slides, and attend live presentations. If you are an attendee, please login to get full access.
x