Smart collections: Can artificial intelligence tools and techniques assist with discovering, evaluating and tagging digital learning resources?

Richard Leibbrandt, Dongqiang Yang, Darius Pfitzner, David Powers, Pru Mitchell, Sarah Hayman, Helen Eddy

Research output: Contribution to conferencePresentationpeer-review

Abstract

This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be employed to help with creation and consistency of learning resource metadata and improve the efficiency of digital collection workflows? The results show some success with automated subject categorisation on a small sample, and the researchers conclude that automated classification based on artificial intelligence is useful as a means of supplementing and assisting human classification, but is not at this stage a replacement for human classification of educational  resources. 
Original languageEnglish
DOIs
Publication statusPublished - 10 Feb 2010
Externally publishedYes
EventIASL Annual Conference Proceedings - Brisbane
Duration: 10 Feb 2010 → …

Conference

ConferenceIASL Annual Conference Proceedings
Period10/02/10 → …

Keywords

  • machine learning
  • Curriculum resources
  • educational metadata

Disciplines

  • Educational Assessment, Evaluation, and Research
  • Computer Sciences
  • Artificial Intelligence and Robotics

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