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Keynote at AgroLT 2008

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Workshop on Learning Technology Standards for Agriculture and Rural Development (AgroLT 2008) …

Workshop on Learning Technology Standards for Agriculture and Rural Development (AgroLT 2008)
September 19, 2008, Athens, Greece
In conjunction with
4th International Conference on Information and Communication Technologies in Bio and Earth Sciences (HAICTA 2008)

Published in: Education, Technology

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  • Transcript

    • 1. Linking learning technology with agricultural knowledge organization schemes Miguel-Angel Sicilia [email_address] University of Alcalá, Madrid, Spain
    • 2. The University of Alcalá
      • Founded by the Regent of Spain, Cardinal Cisneros, 1499
    • 3.  
    • 4. Contents
      • Background: learning objects
      • The Organic.Edunet approach
      • Semantic annotation of learning resources
      • An extended architecture for semantic search of learning resources
    • 5. The talk in a single slide
    • 6.  
    • 7.
      • I. Learning objects (revisited)
    • 8. The status of Learning Technology
      • Widespread adoption of LMSs like Moodle.
      • A philosophy of reusing resources:
        • The “learning object” (LO) paradigm: modularity in educational resources
        • Open courseware (OCW) and open educational resources (OER) as catalysts for the creation of noncommercial on-line learning resources.
      • Search in single repositories and federations of repositories.
    • 9. Learning objects…
      • The Learning Object (LO) paradigm for learning resources has gained a status of maturity, but paradoxically its definition is still subject to debate.
      • Some definitions (McGreal, 2004):
        • Anything and everything.
        • Anything digital, whether it has an educational purpose or not.
        • Anything that has an educational purpose.
        • Only digital objects that have a formal educational purpose.
        • Only digital objects that are marked in a specific way for educational purposes
    • 10. The many definition of learning objects LO – “semantically” annotated
    • 11. Reusability…
      • “ Reusability” is the main driver of LO design.
      • Some typical OER today are of “large” granularity
        • This is not completely true for example, in Connexions .
      • Current standards & specs basically address transportability
    • 12.  
    • 13. Learning technology standards
      • Modest adoption of standards and specs:
        • Some specifications for transportability as SCORM CP (or arguably IMS QTI) have gained a degree of popularity.
        • Some repositories supporting IEEE LOM metadata.
        • Many standards beyond that are still not widely used or supported, e.g. IMS LD.
    • 14. IEEE LOM in a nutshell
      • Multi-part IEEE standard that specifies Learning Object Metadata.
      • Its purpose is to facilitate search, evaluation, acquisition, and use of learning objects
    • 15. IEEE LOM categories
      • The full record of metadata is divided into 9 categories (groups of related data elements):
        • General : general information about the learning object
        • Lifecycle : features related to the history and current state
        • Meta-Metadata : information about the metadata instance itself
        • Technical : technical requirements and characteristics
        • Educational : educational and pedagogic characteristics
        • Rights : intellectual property rights and conditions of use
        • Relation : relationships between this LO and others
        • Annotation : comments on the educational use of the LO (when and by whom the comments were created)
        • Classification : describes this learning object in relation to a particular classification system
    • 16.  
    • 17.
      • II. The Organic.Edunet approach
    • 18. Different needs… ( organic.edunet context)
      • Practical needs:
      • Techniques
      • Methods
      • Best practices
      • Economical constraints
      • Need for pedagogical information:
      • Learning resource type
      • Duration
      • Instructional design issues
      • Need for specific kinds of evidence:
      • Experiments
      • Meta-studies
      • Specific kinds of resources:
      • - Academic papers
    • 19. … require different solutions
      • Shift from “getting more” to “getting less”?
    • 20. Current solutions
      • Web search engine
        • “ universal” scope, often limited by information overload.
        • Quality means (weighted) number of incoming links
      • General purpose repository
        • Adds quality control and sometimes versioning
      • Thematic repository
        • Adds domain-specific quality control
      • Federation of repositories
        • Allows search broadcasting
    • 21. Components in a repository federation
      • A number of repositories provide field-level metadata search (e.g. Merlot, CAREO, dspace, educommons)
      RepositoryBroker
      • In some cases, there is a broker that “blindly” federates the queries.
      Client a repository another repository ln: fertilizer <dc:title>European Fertilizer Manufacturers Assoc. (EFMA)</dc:title> <dc:desc>...</dc:desc> ... http://cms.efma.org/
    • 22. Drawbacks and solutions
      • Two basic issues:
        • Search basically consists in matching character strings in some metadata fields
          • No specificity according to the profile
        • The federation simply broadcasts the queries and puts together the results.
      • There is a need for:
        • More elaboration in the expression and matching of learning needs.
        • Intelligent broadcasting and purposeful fusion of results from several sources
    • 23. Using Knowledge Organization Systems (KOS)…
      • “… classification and categorization schemes that organize materials at a general level , subject headings that provide more detailed access , and authority files that control variant versions of key information such as geographic names”
      • Is a formal ontology a KOS?
        • Can be used as such.
        • They are not necessarily devised to be used as KOS.
    • 24. Using ontologies
      • Ontology: formal, shared schema – not related to lexicon or terminology.
        • In contrast to thesauri, ontologies are not concerned with terminology (lexicon) but with conceptualizations
        • … with the formal conceptualization that best serves “science” (Quine)
        • There can be inconsistent ontologies that share a conceptual subset
          • e.g. to model controversies.
        • Detailed representation allows fine-grained annotation.
          • A success story is the Gene Ontology!
      • The problem from a theoretical perspective:
        • Representing the knowledge of an expert (the one that selects the resources) based on domain knowledge (ontologies)
    • 25. Example
      • A thesaurus term:
        • Fertilizer
      • Which entities is it representing?
        • Fertilizer-SubstanceKind
        • Fertilization-Technique
        • Fertilization-Action
        • Fertilizer-ChemicalComponent
        • Fertilization-OutcomeSituation
    • 26. The ontologies in Organic.Edunet
    • 27. Roles in the ontological model
      • Experts:
        • Decide the elements to include.
        • Decide how to define them.
        • Provide a validating subset of annotated contents.
      • Technical:
        • Provide the technical format.
        • Detect inconsistencies, missing issues.
      • An iterative process!
    • 28. The case of ‘fertilizers’
    • 29. From AGROVOC to ontology
      • What is a “OA” fertilizer?
        • Are OrganicFertilizers disjoint with InorganicFertilizers?.
        • Are (all) SyntheticFertilizers inorganic?
          • Is there any synthetic fertilizer suitable for OA?
        • All natural organic fertilizers are OA fertilizers?
        • “ everything defined in a OA standard”?
    • 30.
      • III. Semantic annotation of Learning resources
    • 31. Classifying learning objects
      • LOM category 9.Classification
      • Purpose: to provide a place to put information that describes the resource in a manner not provided in the meta-data structure elsewhere
      • Classification descriptions are captured within taxonpath , description and keywords
    • 32. Using Agrovoc to classify learning objects
      • AGROVOC: Quarantine (id.6402)
      • Derives from BT: Disease control
      • Purpose: Discipline
      • Taxonpath:
        • Source: Agrovoc
        • Taxon:
          • Id: 2327
          • Entry: Disease control
          • Taxon:
            • Id: 6402
            • Entry: Quarantine
    • 33. Editing LOM metadata
    • 34. Advanced requirements
      • Learning resources are specific kinds of information resources, and there are additional desirable requirements on KOS used for annotation:
        • Predication of pedagogically relevant intentions of the indexing.
        • Consistent degree of granularity of the terms.
        • Separation of content indexing and pedagogical intention.
        • Pedagogical interpretability of relations, i.e. that the relations between the terms represent useful relations for representing learning chunks or relations among learnable elements.
    • 35. Examples
      • R1: “ Types and Uses of Nitrogen Fertilizers for Crop Production ” [1] ,
      • R2: “ Economics of Nitrogen Fertilizer and Crop Production ” [2]
      • Typical indexing
        • Nitrogen fertilizers term, code 5195 in AGROVOC (which has not narrower terms).
      • Predicate-based indexing:
        • R1 discussesApplicationOf AGV5195
        • R2 discussesEconomicsOf AGV5195.
      • In addition, is Fertilizer specific enough?
        • It is not, if the learning objectives specify relevant sub-classes.
        • [1] http://www.ces.purdue.edu/extmedia/AY/AY-204.html
        • [2] h ttp:// www.agrium.com/uploads/nit_prices_and_crop_prod.pdf
    • 36. Examples
      • Reigeluth’s elaboration theory (1999) uses relations as “broader/narrower” between concepts to derive sequencing.
      • The Plant Ontology [1] (PO) is an example of a KOS with clear part-of relations that can be effectively used for that purpose.
        • For example, if we have
          • R3 explainsConcept meristem[PO:0006085]
        • Then, learning object composition can safely proceed to compose with resources about other terms that are related with part_of to the term root (a plant structure in the PO).
    • 37. Technical issues
      • Learning resources ( LearningObject s ) are represented as InformationBearingThing s as elements inside the ontology.
      • A number of predicates relates the LearningObject instances to concepts or instances, e.g:
        • LO1 criticizes someTechnique
      • There are axioms and rules
        • “ Every LO according to CISCO should havePart a ExerciseLearningObject ”
      • So the benefits are: typing + typed predicates + inference
    • 38. AnimalProductionActivity LearningObject Issue AnimalProductionIssue HumanActivity AnimalProductionDerivedActivity Module 2 – Animal production inTheContextOf <learningObjectPredicate> PlantProductionActivity ServiceProductionActivity ActivitySector ProductiveSector AnimalProductionSector isPartOf
    • 39. Coming back to the example…
    • 40.
      • lo1. compares (organicProduction, nonOrganicProduction)
      • lo1. variableUsed (dryWeight)
      • lo1. variableUsed (plantNutrientContent)
      • lo1. objectStudied (organicFertilizer)
      • lo1. about (tomato[PlantType])
      • lo1. about (plantlet[PlantProductionTechnique])
    • 41.
      • IV. An extended architecture for semantic search of learning resources
    • 42. Principles for “Semantic Learning Technology”
      • Domain specificity in search and composition of resources
        • Framework, not single application
      • Different search and composition strategies (different experts) codified as Query Resolvers (QR).
        • For farmers: focus on techniques (how to do)
        • For scientists: focus on research (experiments, studies)
        • For students: consider instructional elements (e.g. exercises)
    • 43. Client a repository ln : fertilizer-application, soil-quality profile : farmer Fertilizer-application-method Soil sampling Measurement-technique isA Variable rate fertilizer application isA Soil quality This as a query string returns nothing in Intute. AGR QR for practitioners http://www.ag.ndsu.edu/pubs/plantsci/soilfert/sf-990.htm
    • 44. Navigating the ontologies…
    • 45. Outlook
      • Semantics can be viewed as an evolution of current metadata tagging
        • They provide an alternative to existing search systems.
        • They capture “more” human knowledge.
        • … but they require new search approaches that will not be widespread immediately.
      • Future work should study the economics of semantic metadata and how they are able to produce better value to users.
    • 46.
      • Thank you!

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