agricultural education collections & repositories: scratching the surface
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Transcript of agricultural education collections & repositories: scratching the surface
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agricultural education collections & repositories
scratching the surface
Nikos Manouselis, Vassilis ProtonotariosAgro-Know Technologies
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(long) introduction
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learning object
"any entity, "any entity, digitaldigital or non-digital, that may or non-digital, that may be used for be used for learninglearning, , educationeducation or or trainingtraining""
ΙΕΕΕ Learning Technology Standards Committee (2002)
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http://www.soilassociation.orghttp://www.soilassociation.org
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http://www.climatecrisis.nethttp://www.climatecrisis.net
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http://www.teachersdomain.orghttp://www.teachersdomain.org
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http://www.digitalgreen.orghttp://www.digitalgreen.org
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learning object
"any entity, "any entity, digitaldigital or non-digital, that may or non-digital, that may be used for be used for learninglearning, , educationeducation or or trainingtraining““
+ metadata describing this use+ metadata describing this use
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Publisher
Date Catalog
SubjectID
AuthorTitle
metadata
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educational metadata
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NOT a learning object
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…a learning object, in context
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metadata reflect the context
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our context
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agricultural science(s)
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lots of data produced & consumed
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what does ag-specific mean?• data types/formats very particular to
agricultural education?• classification of data around agriculture-specific
themes & topics?• connectivity and combination of data with
other sources of agricultural interest?• usage scenarios, environments & tools tightly
connected and specialised for agricultural practices and applications?
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data types & formats• bibliographic metadata• educational content• statistical/economic data• germplasm collections• soil maps• DNA sequence markers/data• …more
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classification schemes• knowledge organisation systems for agriculture
– AGROVOC, CABI, NAL, …
• in recent work, we identified more than 88 ag-specialised ones
[Palavitsinis & Manouselis, “Agricultural Knowledge Organization Systems: analysis of an indicative sample”, in press]
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classification schemes• knowledge organisation systems for agriculture
– AGROVOC, CABI, NAL, …
• in recent work, we identified more than 88 ag-specialised ones
[Palavitsinis & Manouselis, “Agricultural Knowledge Organization Systems: analysis of an indicative sample”, in press]
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connectivity & combination of data • interoperability to achieve remix & reuse
– learning technology standards & specifications
• recently revisited metadata analysis of agricultural learning repositories– 11 out of 13 found implementations have been
analysed– satisfactory conformance to base metadata schemas
was found– next step: harmonization & exchange of good practices[Manolis et al., “Revisiting an analysis of agricultural learning repository metadata: preliminary
results”, MTSR’12]
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connectivity & combination of data Metadata TermsGroup of Common Properties
Property dc:-based dcterms:-based lom:-based More specific Metadata Terms
1. General Characteristics Identifier dc:identifier
dcterms:identifier lom:identifier
Title dc:title dcterms:title lom:title dcterms:alternative
Language dc:languagedcterms:language lom :language
Descriptiondc:description
dcterms:description
lom :description dcterms:abstract
ags:DescriptionNotes Keyword dc:subject dcterms:subject lom:keyword ags:subjectThesaurus2. Life Cycle Entity role dc:creator dcterms:creator lom: role
dc:contributor
dcterms:contributor lom: role
dc:publisherdcterms:publisher lom: role
3. Technical Characteristics Format dc:format dcterms:format lom:format
4. Educational Characteristics
Learning Resource Type dc:type
lom:learningResourceType
5. Intellectual Property Rights
Rights Description dc:rights dcterms:rights lom:rights ags:rightsStatement
dcterms:license
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scenarios & environments• very much context-specific: educational
activity workflows to be carefully studied and modelled
• preliminary ideas currently explored in connection with digital content– e.g. educational scenarios/pathways
http://portal.organic-edunet.eu/index.php?option=com_content&view=article&id=2177&catid=1&Itemid=103
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required: learning repositories
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definitions• digital repository: system for the storage,
location and retrieval of digital resources• digital learning repository (DLR):
– nature of resources or their description reflects an interest of use in an educational context
Holden C., “From Local Challenges to a Global Community: Learning Repositories Summit”, Academic ADL Co-Lab, 2003
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putting it all together• agricultural data/content being stored and
described to serve educational activities– types of data/content that would serve typical
educational needs in this context– metadata that includes proper thematic
classification and ensures interoperability– design & development of educational
scenarios/pathways on top of this content
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interesting (?) questions
• do existing, generic learning repositories have content of agricultural interest?– do they have a lot?
• are there learning repositories focusing particularly to agricultural & rural stakeholders?– where are they?
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preliminary study
• took place during 2005 • examined 59 well-known general-purpose
repositories– found in 27 of them (~45%) agricultural content
BUT• in a total of ~881,000 educational resources:
– …only 3,201 resources (0.36%) related to agricultural topics
Tzikopoulos et al., "Investigating Digital Learning Repositories' Coverage of Agriculture-related Topics", ITAFE 2005.
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0
500
1000
1500S
um
34.5% of resources not 34.5% of resources not particularly classified particularly classified
subject classification
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so we assume that…• learning repositories that particularly
focus on agricultural & rural stakeholders–should probably have more relevant
content–should probably have it better
described/categorized
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technology• content management systems for digital
repositories exist and are very popular– many of them specifically adapted for educational
content (e.g. Dspace, ePrints, Fedora, …)
• some tools already being adapted for the agricultural domain– e.g. AgriOceanDSpace, Organic.ePrints, AgriDrupal, …
• learning management systems also include resource/collection repository component– Moodle (and agriMoodle), ILIAS, …
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technology• content management systems for digital
repositories exist and are very popular– many of them specifically adapted for educational
content (e.g. Dspace, ePrints, Fedora, …)
• some tools already being adapted for the agricultural domain– e.g. AgriOceanDSpace, Organic.ePrints, AgriDrupal, …
• learning management systems also include resource/collection repository component– Moodle (and agriMoodle), ILIAS, …
ADOPTION, AVAILABILITY, O
PENESS
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more problems?
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--indicative list--
a. metadata authoring/creationb. metadata curation/validationc. metadata values/vocabulariesd. metadata multilingualitye. …lots more
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a. authoring/creation
• metadata creation is a painful and costly process–automatic generation can help–high quality/accuracy/relevance
descriptions require human intervention
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a. authoring/creation
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b. curation/validation
• good online services demand high quality (or at least not poor quality) description of content–someone needs to take the final decision
before something is published–especially relevant when content
development has been costly/labourous
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b. curation/validation
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c. values/vocabularies• mappings and crosswalks among values and
vocabularies of different collections are crucial–usually manually defined and maintained–difficult to ensure that all applications will
publish and link their vocabularies–vocabulary bank management tend to become
too complex for the purpose that they serve
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c. values/vocabularies
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d. multilinguality• for multilingual contexts, everything
needs to become (and be maintained) multilingual–metadata values and labels– interface labels for various systems–automatic translation helps but usually
produces rather rough/poor translations
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d. multilinguality
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conclusionconclusion
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potential• learning objects/resources: useful• having them organised in learning
repositories: good • exploring ways to introduce them into
formal and informal education & training–challenging and worthwhile
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challenges• technical issues
–mainly interoperability• content issues
–taking advantage of existing collections– integrate traditional data types/sources
coming from agricultural science–combine with cultural heritage, research
work/outcomes, …
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thank you!