Presentation at the AgEd Workshop 2012 at University of Gastronomic Sciences, Pollenzo, Bra, Italy http://wiki.agroknow.gr/agroknow/index.php/AgEdWorkshop_2012

1. agricultural education
collections & repositories
scratching the surface
Nikos Manouselis, Vassilis Protonotarios
Agro-Know Technologies
nikosm@ieee.org; vprot@agroknow.gr

2. (long) introduction

3. learning object
"any entity, digital or non-digital, that may
be used for learning, education or
training"
ΙΕΕΕ Learning Technology Standards Committee (2002)

4. http://www.soilassociation.org

5. http://www.climatecrisis.net

6. http://www.teachersdomain.org

7. http://www.digitalgreen.org

8. learning object
"any entity, digital or non-digital, that may
be used for learning, education or
training“
+ metadata describing this use

9. metadata
Author Subject
ID Title
Publisher
Date Catalog

10. educational metadata

11. NOT a learning object

12. …a learning object, in context

13. metadata reflect the context

14. our context

15. agricultural science(s)

16. lots of data produced & consumed

17. 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?

18. data types & formats
• bibliographic metadata
• educational content
• statistical/economic data
• germplasm collections
• soil maps
• DNA sequence markers/data
• …more

19. 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]

20. 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]

21. 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]

22. connectivity & combination of data
Metadata Terms
Group of More specific
Common Property dc:-based dcterms:-based lom:-based Metadata Terms
Properties
1. General
Characteristics 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:subjectThesaurus
2. Life Cycle Entity role dc:creator dcterms:creator lom: role
dc:contributor dcterms:contributor lom: role
dc:publisher dcterms:publisher lom: role
3. Technical
Characteristics Format dc:format dcterms:format lom:format
4. Educational Learning lom:learningReso
Characteristics Resource Type dc:type urceType
5. Intellectual
Property Rights
Rights Description dc:rights dcterms:rights lom:rights ags:rightsStatement
dcterms:license

23. 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

24. required:
learning repositories

25. 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

26. 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

27. 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?

28. 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.

29. 1500
34.5% of resources not
1000 particularly classified
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30. 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

31. 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, …

32. 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, …)
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• 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
• learning
A Dresource/collection repository component
– Moodle (and agriMoodle), ILIAS, …

33. more problems?

34. --indicative list--
a. metadata authoring/creation
b. metadata curation/validation
c. metadata values/vocabularies
d. metadata multilinguality
e. …lots more
34

35. a. authoring/creation
• metadata creation is a painful and
costly process
– automatic generation can help
– high quality/accuracy/relevance
descriptions require human intervention
35

36. a. authoring/creation
36

37. 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

38. b. curation/validation
38

39. 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

40. c. values/vocabularies
40

41. 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

42. d. multilinguality
42

43. conclusion

44. 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

45. 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, …

46. thank you!