Intelligent learning object guide (iLOG): A framework for automatic empirically-based metadata generation

S. A. Riley, L. D. Miller, Leen-Kiat Soh, Ashok K Samal, Gwen C Nugent

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

We present a framework for the automatic annotation of learning objects (LOs) with empirical usage metadata. Our implementation of the Intelligent Learning Object Guide (iLOG) was used to collect interaction data of over 200 students' interactions with eight LOs. We show that iLOG successfully tracks student interaction data that can be used to automate the creation of meaningful empirical usage metadata that is based on real-world usage and student outcomes.

Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
Pages515-522
Number of pages8
Edition1
ISBN (Print)9781607500285
DOIs
Publication statusPublished - Jan 1 2009

Publication series

NameFrontiers in Artificial Intelligence and Applications
Number1
Volume200
ISSN (Print)0922-6389

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Keywords

  • Association Rule Mining
  • Empirical Usage Metadata
  • Feature Selection
  • Learning Objects
  • SCORM

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Riley, S. A., Miller, L. D., Soh, L-K., Samal, A. K., & Nugent, G. C. (2009). Intelligent learning object guide (iLOG): A framework for automatic empirically-based metadata generation. In Frontiers in Artificial Intelligence and Applications (1 ed., pp. 515-522). (Frontiers in Artificial Intelligence and Applications; Vol. 200, No. 1). IOS Press. https://doi.org/10.3233/978-1-60750-028-5-515