Smart collections

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Presentation at International Association of School Librarianship Research Forum, describing 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?

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Smart collections

  1. 1. Smart collections<br />Pru Mitchell & Richard Leibbrandt<br />pru.mitchell@esa.edu.au<br />leib0006@csem.flinders.edu.au<br />
  2. 2. The goal<br />To describe resources for education so learners and educators can find<br />the right resource at the right time<br />
  3. 3. every reader his/her book<br />Ranganathan 1931, 5 principles of library science<br />
  4. 4.
  5. 5.
  6. 6. Educational value<br />Curriculum relevance<br />Intended user<br />Educational level<br />Educational quality<br />How can I use this for learning?<br />
  7. 7.
  8. 8. Research question<br />Can artificial intelligence tools and techniques assist with discovering, evaluating and tagging digital learning resources?<br />
  9. 9. Research partners<br />Flinders University Artificial Intelligence and Language Lab<br />V.A.L.I.A.N.T.<br />Education Network Australia (edna)<br />
  10. 10. Proof of concept<br />3 people<br />$30,000<br />3 months <br />3 show and tells<br />blog for peer review<br />
  11. 11. Research team<br />Dr Richard Leibbrandt<br />Dr Dongqiang Yang<br />Darius Pfitzner<br />Prof David Powers<br />Pru Mitchell<br />Sarah Hayman<br />Helen Eddy<br />
  12. 12. Technical concepts<br />artificial intelligence<br />text classification (TC)<br />semantic annotation<br />Text Frequency/Inverse Document Frequency (TFIDF)<br />semantic web<br />taxonomy and folksonomy<br />
  13. 13. DSpace metadata tool<br />Text<br />Extraction tool<br />Subject Keywords<br />Resource<br />edna Categories<br />html<br />edna-userlevel<br />edna-audience<br />Educational use elements<br />
  14. 14. Key phrase extraction<br />a (the full text document)<br />? <br />b (keywords, audience, userlevel)<br />Which terms in the text prompt this decision?<br />
  15. 15.
  16. 16.
  17. 17. Findings<br />labour-intensive challenge<br />subject classification easier than userlevel<br />audience and user level indicated by <br />meaning, vocabulary, choice of words, style, font size, graphic elements, layout<br />‘reading’ web pages is a complex literacy<br />
  18. 18. Semantic network<br />Automated subject analysis<br />Can system predict edna category?<br />Tools<br />WordNetWikipediaDbpedia<br />
  19. 19.
  20. 20.
  21. 21.
  22. 22. Findings<br />
  23. 23. User terms to thesaurus<br />Schools Online Thesaurus<br />scot.curriculum.edu.au<br />Can system predict ScOT terms from teacher keywords?<br />
  24. 24. User metadata<br />the only group that can categorize everything is everybody<br />Clay Shirky, 2005Ontology is overrated<br />
  25. 25. Mapping teacher tags<br />Proper nouns & brands<br />.NET, iPod, excel<br />Semantic ambiguity<br />notes -> Pitch (Music)<br />Stemming<br />practice exams -> Practical Examinations<br />
  26. 26. Findings<br />% ScOT terms predicted from keywords<br />
  27. 27. Benefits<br />Efficiency of cataloguing through classification suggestions<br />Improved user experience through more relevant and consistent search results<br />Improved integration of user contributed resources if tags are mapped to taxonomy<br />
  28. 28. Conclusions<br />Artificial intelligence systems showed some success in subject categorisation of text-based digital learning resources<br />Key phase extraction to support subject categorisation was more successful than of audience and user-level categorisation<br />
  29. 29. Summary<br />Automated classification based on artificial intelligence may be useful as a means of supplementing and assisting human classification, but is not at this stage a replacement for human classification<br />
  30. 30. Future <br />ScOT development<br />Achievement Standards Network<br />Machine Readable Curricula<br />Multilingual thesauri<br />OER ‘travelling well’ globally<br />
  31. 31. Credits<br />Foyer artwork 2008, State Library of South Australia http://www.slsa.sa.gov.au/site/page.cfm?u=409<br />Climate change timeline, Copyright ABC<br />http://www.abc.net.au/environment/timeline.html<br />Holeymoon 2008, Rent 1, 2, 3 CC-by-nc-sahttp://www.flickr.com/photos/holeymoon/2926989641<br />International Standard Book Number, Wikipedia, CC-by-sahttp://en.wikipedia.org/wiki/International_Standard_Book_Number<br />Leave your mark, Oxfam Australia<br />http://www.leaveyourmark.my3things.org<br />O’Connor, D 2005 Binary Finary, CC-by-nc-sahttp://www.flickr.com/photos/clockwerx/9267076<br />Ranganathan, S 1931, The five laws of library science The Madras Library Association, London: Goldston, Madras<br />Yelkrokoyade 2010, Conservera de Lisboa , CC-sahttp://commons.wikimedia.org/wiki/File:Conservera_de_Lisboa.jpg<br />
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