IASL Research Abstracts: 330

330. Leibbrandt, R., Yang, D., Pfitzner, D., Powers, D., Mitchell, P., Hayman, E., and Eddy, H. (2010). Smart collections: can artificial intelligence tools and techniques assist with discovering, evaluating and tagging digital learning resources?

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.

Subject categories: 1, 2, 5, 9, 11, 23
Country: Australia

Abstract Submitted by Angela Austin, Florida State University