agricultural education collections & repositories: scratching the surface
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agricultural education collections & repositories: scratching the surface

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Presentation at the AgEd Workshop 2012 at University of Gastronomic Sciences, Pollenzo, Bra, Italy

Presentation at the AgEd Workshop 2012 at University of Gastronomic Sciences, Pollenzo, Bra, Italy

http://wiki.agroknow.gr/agroknow/index.php/AgEdWorkshop_2012

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agricultural education collections & repositories: scratching the surface agricultural education collections & repositories: scratching the surface Presentation Transcript

  • agricultural educationcollections & repositories scratching the surface Nikos Manouselis, Vassilis Protonotarios Agro-Know Technologies nikosm@ieee.org; vprot@agroknow.gr
  • (long) introduction
  • learning object"any entity, digital or non-digital, that may be used for learning, education or training" ΙΕΕΕ Learning Technology Standards Committee (2002)
  • http://www.soilassociation.org
  • http://www.climatecrisis.net
  • http://www.teachersdomain.org
  • http://www.digitalgreen.org
  • learning object"any entity, digital or non-digital, that may be used for learning, education or training“+ metadata describing this use
  • metadata Author SubjectID Title Publisher Date Catalog
  • educational metadata
  • NOT a learning object
  • …a learning object, in context
  • metadata reflect the context
  • our context
  • agricultural science(s)
  • lots of data produced & consumed
  • 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?
  • data types & formats• bibliographic metadata• educational content• statistical/economic data• germplasm collections• soil maps• DNA sequence markers/data• …more
  • 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]
  • 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]
  • 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]
  • connectivity & combination of data Metadata TermsGroup of More specificCommon Property dc:-based dcterms:-based lom:-based Metadata TermsProperties1. GeneralCharacteristics Identifier dc:identifier dcterms:identifier lom:identifier   Title dc:title dcterms:title lom:title dcterms:alternative Language dc:language dcterms:language lom :language   Description dc: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:publisher dcterms:publisher lom: role  3. TechnicalCharacteristics Format dc:format dcterms:format lom:format  4. Educational Learning lom:learningResoCharacteristics Resource Type dc:type   urceType  5. IntellectualProperty RightsRights Description dc:rights dcterms:rights lom:rights ags:rightsStatement dcterms:license
  • 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/pathwayshttp://portal.organic-edunet.eu/index.php?option=com_content&view=article&id=2177&catid=1&Itemid=103
  • required:learning repositories
  • 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
  • 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
  • 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?
  • preliminary study• took place during 2005• examined 59 well-known general-purpose repositories – found in 27 of them (~45%) agricultural contentBUT• 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.
  • 1500 34.5% of resources not 1000 particularly classifiedmSu 500 0 Fo Ec Fi So r o sh il S Ag C Liv Pl N es Fo no er ro at an try m ies cie ric od es ur ps ,F ics td nc al ult to or ,T e an is R ck es ur ea ra d es t de e H se Pr ou or od an s, rc tic uc d W es ul ts Ru ee tu an an ra re ds d lD d ,a Ag ev Pl ro e nd an subject fo lop tS Pe re m str en ci st y en t C ce on tro l classification
  • 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
  • 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, …
  • technology • content management systems for digital E SS repositories exist and are very popular EN – many of them specifically adapted forO P Y , educational IT content (e.g. Dspace, ePrints, Fedora, …) IL AB • some tools already being adapted for the agricultural domainIL VA Organic.ePrints, ,A – e.g. AgriOceanDSpace, N IO AgriDrupal, … O PT management systems also include • learningA Dresource/collection repository component – Moodle (and agriMoodle), ILIAS, …
  • more problems?
  • --indicative list--a. metadata authoring/creationb. metadata curation/validationc. metadata values/vocabulariesd. metadata multilingualitye. …lots more 34
  • a. authoring/creation• metadata creation is a painful and costly process – automatic generation can help – high quality/accuracy/relevance descriptions require human intervention 35
  • a. authoring/creation 36
  • 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 37
  • b. curation/validation 38
  • 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 39
  • c. values/vocabularies 40
  • 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 41
  • d. multilinguality 42
  • conclusion
  • 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
  • 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, …
  • thank you!