How can we build an open and scalable learning infrastructure for food safety?
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The document discusses the development of an open and scalable learning infrastructure for food safety aimed at training small food producers globally through blended approaches. It outlines a multi-donor trust fund, the creation of a curriculum and course registry, and an educational offerings aggregator to facilitate access to quality food safety education and resources. The emphasis is on addressing challenges such as metadata creation, validation, and multilingual support while fostering collaboration among various stakeholders in food safety education.
How can we build an open and scalable learning infrastructure for food safety?
- 1. How can we build an open and scalable learning infrastructure for food safety Nikos Manouselis nikosm@agroknow.gr
- 2. http://wiki.agroknow.gr “meaningful services around high-quality agricultural data”
- 4. ?
- 5. ?
- 6. !
- 7. How can I provide safe food?
- 8. How can I produce safe food?
- 10. ?
- 11. 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
- 12. global food safety capacity building
- 16. is it a… ?
- 17. 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
- 18. 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)
- 19. could look like this
- 20. 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
- 21. 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
- 22. 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
- 23. 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, …)
- 24. 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
- 26. 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
- 31. 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
- 32. what’s really happening behind
- 33. 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
- 34. Exposes metadata through OAI-PMH Exposes metadata through OAI-PMH Indexed & available in back-end METADATA AGGREGATOR Exposes metadata through OAI-PMH
- 36. typical problems a. b. c. d. e. metadata authoring/creation metadata assurance/validation metadata values/vocabularies metadata multilinguality …lots more 36
- 37. a. authoring/creation • metadata creation is a painful and costly process – automatic generation can help – high quality/accuracy/relevance descriptions require human intervention 37
- 39. 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
- 41. 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
- 43. 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
- 46. describing course offerings • what I am describing is different to what you are describing… – …but there are so many similar things!
- 47. Africa Lead course database Provider: University of Pretoria
- 49. CerOrganic Schema Africa Lead Schema Missing
- 50. 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!
- 52. AGRICOM job profile description Job description or single task description Required competences
- 53. GFSI Competency Framework AGRICOM Competence Framework / Job task Job / task title Job profile / task description Competence Title Competence description Course title
- 54. 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!
- 55. 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.
- 56. 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
- 57. wrap up
- 58. targeted domain • rich in data-oriented problems and cases • focused on “real” users • inter-disciplinary work • results related to societal goals/challenges
- 59. 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