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How can we build an open and scalable learning infrastructure for food safety?
 

How can we build an open and scalable learning infrastructure for food safety?

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Invited lecture given at the University of Piraeus, focusing on a large scale case study of a learning technologies' application. Focused on the example of the Global Food Safety Partnership (GFSP, ...

Invited lecture given at the University of Piraeus, focusing on a large scale case study of a learning technologies' application. Focused on the example of the Global Food Safety Partnership (GFSP, http://www.gfsp.org) and presented our view on backing it up with an infrastructure federating and linking different information sources/providers. These ideas have also been presented at this JALN paper: http://sloanconsortium.org/jaln/v17n2/open-and-scalable-learning-infrastructure-food-safety

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    How can we build an open and scalable learning infrastructure for food safety? How can we build an open and scalable learning infrastructure for food safety? Presentation Transcript

    • How can we build an open and scalable learning infrastructure for food safety Nikos Manouselis nikosm@agroknow.gr
    • http://wiki.agroknow.gr “meaningful services around high-quality agricultural data”
    • “…why do I care??”
    • ?
    • ?
    • !
    • How can I provide safe food?
    • How can I produce safe food?
    • ?
    • Multi-Donor Trust Fund (MDTF) being established to raise at least $45,000,000 for implementation of a Roadmap and 5-year workplan aim: train small food producers, around the world, using blended approaches
    • global food safety capacity building
    • is it a… ?
    • reflecting on complexity #3 Program Facilitation Open & Scalable Open & Scalable Learning Infrastructure Learning Infrastructure Includes : : Includes - -OER data pool OER data pool - -Course registry Course registry - -Curriculum Development Curriculum Development -Curricula Alignment -Curricula Alignment
    • evolving the concept • may aim higher than a single GFSP learning platform – cannot generalize something that requires a focused, regional approach • rather develop a GFSP Learning Infrastructure including (among others) – Educational Offerings Aggregator Services – Curriculum Support, Registry & Alignment Services – GFSP Learning Portal (main front end) – GFSP Learning Widgets/apps (to be integrated in web sites, offered through smartphones/tablets, etc)
    • could look like this
    • Educational Offerings Aggregator • back end technology infrastructure – ingest, harvest, aggregate course and OER metadata from existing or new (e.g. legacy) learning platforms and OER collections – tools to allow course and OER providers to align/map their metadata & classifications to the GFSP ones
    • Curriculum Registry & Alignment • representation of GFSP curriculum in interoperable format (using learning outcomes, competences) – tools to allow other course providers to register and express/map their curricula to the GFSP curriculum – tools to facilitate the generation of multilingual versions of the curricula descriptions – generation of transformable curricula representations to allow users to browse using preferred curicullum format
    • GFSP Learning Portal • main front-end to present project and allow users to find information in the aggregated sources – various modalities (visual, device, thematic, geographical, industry, …) for search & discovery of courses and OER – multilingual interfaces and metadata facilitated by automatic translation engines
    • GFSP Learning Widgets/Apps • search/discovery interfaces and mechanisms that can be embedded in other web sites and portals (widget-like or search pages in sites) • mobile apps for various operational systems (iOS, Android, Windows 8) • back-end engine to allow straightforward generation of adaptable versions of both (thematic, industry, geographical, linguistic, …)
    • important distinction • such a learning infrastructure is heavily dependent on the back-end layers • it is important to be able to power existing applications and services • the centralised portal mainly serves as demonstrator • will really change something if it provides a wealth of resources around each topic
    • a case study • regional meat producer in Paraguay – example scenario: exploring how their company can start selling packaged cooked ham to an international food distribution company • product of high quality one, made from pure pork ham – let us assume that they would like to find out more about the food safety standards of cooked ham
    • this is why #3 Program Facilitation Open & Scalable Open & Scalable Learning Infrastructure Learning Infrastructure Includes : : Includes - -OER data pool OER data pool - -Course registry Course registry - -Curriculum Development Curriculum Development -Curricula Alignment -Curricula Alignment
    • what’s really happening behind
    • CONTENT PROVIDER WITH UNORGANISED COLLECTION (e.g. Listed at Web site or in DVD-ROM) Chooses compliant tool Metadata export in Ingestion in proprietary format & compliant tool provides mapping CONTENT PROVIDER WITH CMS THAT DOES NOT SUPPORT OAIPMH (e.g. Proprietary DB) CONTENT PROVIDER WITH CMS THAT SUPPORTS OAI-PMH (e.g. FSKN compliant, ePrints, DSPACE,...) DOMAIN EXPERTS publish & evolve vocabularies & ontologies
    • Exposes metadata through OAI-PMH Exposes metadata through OAI-PMH Indexed & available in back-end METADATA AGGREGATOR Exposes metadata through OAI-PMH
    • typical problems a. b. c. d. e. metadata authoring/creation metadata assurance/validation metadata values/vocabularies metadata multilinguality …lots more 36
    • a. authoring/creation • metadata creation is a painful and costly process – automatic generation can help – high quality/accuracy/relevance descriptions require human intervention 37
    • a. authoring/creation 38
    • b. assurance/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 39
    • b. assurance/validation 40
    • 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 41
    • c. values/vocabularies 42
    • 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 43
    • d. multilinguality 44
    • challenges in semantics
    • describing course offerings • what I am describing is different to what you are describing… – …but there are so many similar things!
    • Africa Lead course database Provider: University of Pretoria
    • CerOrganic portal
    • CerOrganic Schema Africa Lead Schema Missing
    • competencies description • what I would like to learn is what you need me to know… – …but what is really needed is connecting a job profile to the relevant course offerings!
    • GFSI Competency Framework
    • AGRICOM job profile description Job description or single task description Required competences
    • GFSI Competency Framework AGRICOM Competence Framework / Job task Job / task title Job profile / task description Competence Title Competence description Course title
    • expected learning outcomes • what I am going to learn should be what I am expected to know for my job… – …but sometimes it’s not very clear what this is going to be!
    • CerOrganic Curriculum description DICLA training center Capabilities: When completing this course you will be able to perform basic routine operations in a defined hydroponic context under close supervision.
    • more issues… • old-fashioned legacy systems still used in such traditional settings – terms like “OER” and “MOOC” sound like science fiction • novel technologies such as semantic stores and ontology editing/managing environments are not user-friendly and proven – especially for such technology-ignorant users • very rich semantics to be represented, handled and exploited; but we are not there yet
    • wrap up
    • targeted domain • rich in data-oriented problems and cases • focused on “real” users • inter-disciplinary work • results related to societal goals/challenges
    • increase use & reuse • digital sources and collections material to be used (and potentially re-used) in several contexts – even different than originally expected/thought of
    • thank you! info@agroknow.gr http://wiki.agroknow.gr