Smart collections
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Transcript of Smart collections
The goal
To describe resources for education so learners and educators can findthe right resource at the right time
every reader his/her book
Ranganathan 1931, 5 principles of library science
Educational valueCurriculum relevance
Intended userEducational level
Educational quality
How can I use this for learning?
Research questionCan artificial intelligence tools and techniques assist with discovering,
evaluating and tagging digital learning resources?
Research partnersFlinders University
Artificial Intelligence and Language LabV.A.L.I.A.N.T.
Education Network Australia (edna)
Proof of concept3 people$30,000
3 months 3 show and tells
blog for peer review
Research team
Dr Richard LeibbrandtDr Dongqiang YangDarius PfitznerProf David Powers
Pru MitchellSarah HaymanHelen Eddy
Technical conceptsartificial intelligence
text classification (TC)semantic annotation
Text Frequency/Inverse Document Frequency (TFIDF)
semantic webtaxonomy and folksonomy
html
Resource
Extraction tool
edna Categories
edna-audience
Subject Keywords
edna-userlevel
DSpace metadata
toolText
Educational use elements
Key phrase extractiona (the full text document)
?
b (keywords, audience, userlevel)
Which terms in the text prompt this decision?
Findingslabour-intensive challengesubject classification easier than userlevelaudience 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?Tools
WordNet Wikipedia Dbpedia
Findings% hits in top-ranked options when predicting edna category
%
Title only 35%Subject without broad term expansion 60%
Subject with broad term expansion 45%
User terms to thesaurus
Schools Online Thesaurusscot.curriculum.edu.au
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 tagsProper nouns & brands .NET, iPod, excelSemantic ambiguity notes → Pitch (Music)Stemming practice exams → Practical Examinations
FindingsMapping approach RecallFull mapping 35.9%Partial mapping 43.1%Semantic mapping 22.8%
% ScOT terms predicted from keywords
Benefits1. Efficiency of cataloguing through
classification suggestions2. Improved user experience through more
relevant and consistent search results3. Improved integration of user contributed
resources if tags are mapped to taxonomy
ConclusionsArtificial 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
SummaryAutomated 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 developmentAchievement Standards NetworkMachine Readable CurriculaMultilingual thesauriOER ‘travelling well’ globally
CreditsFoyer artwork 2008, State Library of South Australia
http://www.slsa.sa.gov.au/site/page.cfm?u=409 Climate change timeline, Copyright ABC
http://www.abc.net.au/environment/timeline.html Holeymoon 2008, Rent 1, 2, 3 CC-by-nc-sa
http://www.flickr.com/photos/holeymoon/2926989641 International Standard Book Number, Wikipedia, CC-by-sa
http://en.wikipedia.org/wiki/International_Standard_Book_Number Leave your mark, Oxfam Australia
http://www.leaveyourmark.my3things.org O’Connor, D 2005 Binary Finary, CC-by-nc-sa
http://www.flickr.com/photos/clockwerx/9267076 Ranganathan, S 1931, The five laws of library science The Madras Library Association,
London: Goldston, MadrasYelkrokoyade 2010, Conservera de Lisboa , CC-sa
http://commons.wikimedia.org/wiki/File:Conservera_de_Lisboa.jpg