smart collections

Smart collections

Pru Mitchell & Richard Leibbrandtpru.mitchell@esa.edu.au

leib0006@csem.flinders.edu.au

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