Smart collections
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Smart collections



Presentation at International Association of School Librarianship Research Forum, describing a joint proof of concept project undertaken by researchers from the Flinders University Artificial ...

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 Smart collections Presentation Transcript

  • Smart collections
    Pru Mitchell & Richard Leibbrandt
  • The goal
    To describe resources for education so learners and educators can find
    the right resource at the right time
  • every reader his/her book
    Ranganathan 1931, 5 principles of library science
  • Educational value
    Curriculum relevance
    Intended user
    Educational level
    Educational quality
    How can I use this for learning?
  • Research question
    Can artificial intelligence tools and techniques assist with discovering, evaluating and tagging digital learning resources?
  • Research partners
    Flinders University Artificial Intelligence and Language Lab
    Education Network Australia (edna)
  • Proof of concept
    3 people
    3 months
    3 show and tells
    blog for peer review
  • Research team
    Dr Richard Leibbrandt
    Dr Dongqiang Yang
    Darius Pfitzner
    Prof David Powers
    Pru Mitchell
    Sarah Hayman
    Helen Eddy
  • Technical concepts
    artificial intelligence
    text classification (TC)
    semantic annotation
    Text Frequency/Inverse Document Frequency (TFIDF)
    semantic web
    taxonomy and folksonomy
  • DSpace metadata tool
    Extraction tool
    Subject Keywords
    edna Categories
    Educational use elements
  • Key phrase extraction
    a (the full text document)
    b (keywords, audience, userlevel)
    Which terms in the text prompt this decision?
  • Findings
    labour-intensive challenge
    subject classification easier than userlevel
    audience and user level indicated by
    meaning, vocabulary, choice of words, style, font size, graphic elements, layout
    ‘reading’ web pages is a complex literacy
  • Semantic network
    Automated subject analysis
    Can system predict edna category?
  • Findings
  • User terms to thesaurus
    Schools Online Thesaurus
    Can system predict ScOT terms from teacher keywords?
  • User metadata
    the only group that can categorize everything is everybody
    Clay Shirky, 2005Ontology is overrated
  • Mapping teacher tags
    Proper nouns & brands
    .NET, iPod, excel
    Semantic ambiguity
    notes -> Pitch (Music)
    practice exams -> Practical Examinations
  • Findings
    % ScOT terms predicted from keywords
  • Benefits
    Efficiency of cataloguing through classification suggestions
    Improved user experience through more relevant and consistent search results
    Improved integration of user contributed resources if tags are mapped to taxonomy
  • Conclusions
    Artificial intelligence systems showed some success in subject categorisation of text-based digital learning resources
    Key phase extraction to support subject categorisation was more successful than of audience and user-level categorisation
  • Summary
    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
  • Future
    ScOT development
    Achievement Standards Network
    Machine Readable Curricula
    Multilingual thesauri
    OER ‘travelling well’ globally
  • Credits
    Foyer artwork 2008, State Library of South Australia
    Climate change timeline, Copyright ABC
    Holeymoon 2008, Rent 1, 2, 3 CC-by-nc-sa
    International Standard Book Number, Wikipedia, CC-by-sa
    Leave your mark, Oxfam Australia
    O’Connor, D 2005 Binary Finary, CC-by-nc-sa
    Ranganathan, S 1931, The five laws of library science The Madras Library Association, London: Goldston, Madras
    Yelkrokoyade 2010, Conservera de Lisboa , CC-sa