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Future of Metadata and Learning Objects
The document discusses trends in metadata and learning objects. It argues that metadata needs to move beyond just describing content to also include people, competencies, locations and contexts. Learning objects should combine content with competencies and contexts to enable personalized learning experiences. Standards like CORDRA aim to support discovery, delivery and identification of learning content across different systems.
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Future of Metadata and Learning Objects
- 1. The Future ofMetadata & Learning Objects Towards a more holistic perspective on learning and performance International Conference on Digital Archive Technology Taipei, Taiwan Oct. 19, 2006
- 2. [email_address] Strategic FuturistPresident & Co-Founder Learnativity.org Chair, IEEE Learning Technology Standards Committee Learning Object Metadata 0 Strategic Advisor
- 3.
- 4. Grand Vision Ina word? PERSONALIZATION (Or if you insist on a prefix call it..) me Learning! Personalized Learning Experiences for every person every day all 6.4 billion of us -- every day!! Just for me and just right: Time, place, amount, device, medium, way… On demand, adaptive Markets of one: Billions of Markets Learning in ALL forms: Formal AND informal not just online, on computer, on screen, etc, but every where, every time “ When the learner is ready the ‘teacher’ will appear.”
- 5. Maybe Goldilocks hadit right all along? The ideal is “just right”…. Just the right CONTENT , to Just the right PERSON , with Just the right PARTNERS , at Just the right TIME , on Just the right DEVICE , in Just the right CONTEXT , and Just the right WAY ……… UP
- 6. “ C -ingthe Future” C ontent, C ompetencies C ontext LEARNING & PERFORMANCE CONTENT CONTEXT COMPETENCE
- 7. “ C-ing” theFuture C ontent Evolving toward a supply chain model Repositories, discovery, assembly Dynamic assembly C ompetencies Are following similar exponential curve and revolution as content Link to content objects OBJECTIVES are the critical “connective tissue”!! C ontext Location based learning Subjective metadata, pattern recognition, etc.
- 8. It all dependsupon…………….. METADATA CONTENT CONTEXT COMPETENCE
- 9. Metadata: Thepast 1997 tipping point, creation of IMS IEEE LTSC ADL A model of standardized modularity emerged as the best way to enable mass customization and adoption Eg no mandatory fields in LOM Accredited standards completed LOM, LOM XML,
- 10. Metadata: ThePresent Broad spread adoption, globally and universally Eg. Tremendous adoption within Taiwan Microsoft announcement EU support (CEN ISSS) Implementation and adaptation is the order of the day Relatively stable Application profiles DOI’s and the whole unique identifier issues Joint activities: ISO SC36? LOM/DCMI Abstract Data model with RDF LOM/RDA CORDRA
- 11. networking, nottaking over repositories Slide courtesy Erik Duval, KU Leuven
- 12. Federated Metadata Slidecourtesy Erik Duval, KU Leuven
- 13. Metadata: CurrentChallenges too exposed to end users Not localized or adopted to context of use Eg direct use of LOM terms Too manual Forms must die! Not flexible enough Too focused on “just” the content Poor utility behond text Not enough tools & technology for the masses Not integrated into workflow and habits Eg. Replacing directories & file names with metadata
- 14. Metadata: beyonddata All the nouns; people, places and things People: Competencies skills, knowledge, abilities, Location Eg. GIS, 3D info such as height Merges into context Context
- 15. Metadata: TheFuture Superseding the standard definition of metadata as “data about data” to include all nouns Implicit and inferred metadata Visualization Going beyond text! Excellent examples at ICDAT 2006 such as with video & audio AUGMENTED Automated metadata generation (AMG) Manual mass contribution of metadata from the masses Manual metadata from the experts Contextual metadata especially that from within learning & working environments Attention metadata Amazon gifts example
- 16. Metadata: TheFuture Transparency and invisibility Metadata models to replace historic meta-categorization Eg automated and transparent directories & file names Integrated into applications Automated simply a part or the “save” function including context see MS announcement re SCORM in Office 2007 Making metadata transparent or invisible to users Federated AND Standards based Standards based but agnostic! Growing attention on Competencies New IEEE Study Groups Sign up for online forum at http://groups.yahoo.com/group/StudyGroup-Competencies
- 17. Visualizing reuse http://ariadne.cs.kuleuven.be/infovis/ Slide courtesy Erik Duval, KU Leuven
- 18. Federated AMG Enginehttp://ariadne.cs.kuleuven.be/amg/ Slide courtesy Erik Duval, KU Leuven
- 19. Attention Metadata http://ariadne.cs.kuleuven.ac.be/empirical/ Slide courtesy Erik Duval, KU Leuven
- 20. User feedback Slidecourtesy Erik Duval, KU Leuven
- 21. Contextual and AutomatedMetadata Generation (AMG) http://ariadne.cs.kuleuven.be/amg/ Slide courtesy Erik Duval, KU Leuven
- 22. scaleable remix Slidecourtesy Erik Duval, KU Leuven
- 23. Hiding everything butthe benefits http://ariadne.cs.kuleuven.be/alocom/ Slide courtesy Erik Duval, KU Leuven
- 24. A glimpse ofthe future? Learning from other domains: Music? The “genome” model Pandora and the Music Genome Project “ help me discover more music I will like” Note that it avoids subjective categorization Currently dependent upon trained “experts” and manual evaluation but can and must be automated What of a “Content Genome” and a “Competencies Genome”??
- 25. Pandora and theMusic Genome Project
- 26. “ Individualized” CareerDevelopment The Navy “5 Vector Model” Universal Quals Apprentice Journeyman Master Recruit EM Quals First line Leader Foundational Leader Primary Leader Advanced Leader Command Leader Executive Leader Platform Career Options EM Gas Turbine 5VM (DDG-51) Engineering Quals PROFESSIONAL DEVELOPMENT PERSONAL DEVELOPMENT LEADERSHIP CERTS | QUALS PERFORMANCE Industry Certs Personal Dev SO Personal Dev SO Personal Dev SO Lifelong Learning Lifelong Learning Lifelong Learning Master S/O Journeyman S/O Apprentice S/O E-1 E-2 E-3 E-4 E-5 E-6 E-7 E-8 E-9 Recruit S/O EM2 Sciulli Universal Quals Engineering Quals EM Quals Universal Quals Engineering Quals EM Quals Industry Certs Industry Certs Industry Certs Apprentice Positions Journeyman Positions Master Positions Selected Electricians All Electricians
- 27. LEARNING OBJECTS Theworld of CONTENT
- 28. Learning Objects: The Past Learning Objects initially proposed in 1992 with LALO A Lego model of modularity
- 29. Learning Objects: The Present Enormous popularity of the term and the basic approach of modularity Growing demand for dynamic “just the right” content Critical role of accredited and other standards
- 30. Learning Objects: Myths & Misunderstandings Can’t have both Reusability AND context No such thing as RLO’s! how small the level of granularity is the roles of richness and metadata in the model the “nested” nature of the model the ability to deliver both maximum “context” (learning) and maximum reusability and repurposing the degree of mass customization and ultimately personalization this enables the connection to skills and competencies through objectives
- 31. Learning Objects: Challenges Have not hit the tipping point of modularity yet Separating content down to “raw” level Treating content as an island, no inclusion of Context and Competencies yet Lack of shareable and reusable OBJECTIVE statements
- 32. Universal Object Model?Principle Fact Process Overview Procedure Text Audio Summary Concept ENABLING Objective E nabling L earning O bject T erminal L earning O bject Animation Simulation illustration TERMINAL Objective Common Content Application Specific Profiles Repurposed with Permission: W.Hodgins ©1992 Learnativity “ Raw” Data & Media Elements Information Blocks CONTENT ASSETS 0 SkillObject
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- 34. Universal Object Model?Principle Fact Process Overview Procedure Text Audio Summary Concept Principle Process Concept Procedure Fact Overview Summary Objective E nabling L earning O bject T erminal L earning O bject Collections ( Courses , Stories,) Animation Simulation illustration Objective Theme Enabling Objective Terminal Objective Common Content Application Specific Profiles Repurposed with Permission: W.Hodgins ©1992 Learnativity “ Raw” Data & Media Elements Information Blocks CONTENT ASSETS 0 SkillObject
- 35. Universal Object Model?Principle Fact Process Overview Procedure Text Audio Summary Concept Principle Process Concept Procedure Fact Overview Summary Objective E nabling L earning O bject T erminal L earning O bject Collections ( Courses , Stories,) Animation Simulation illustration Objective Theme Enabling Objective Terminal Objective Common Content Application Specific Profiles Repurposed with Permission: W.Hodgins ©1992 Learnativity “ Raw” Data & Media Elements Information Blocks CONTENT ASSETS 0 SkillObject CONTEXT ReUSABILITY
- 36. Learning Objects: The Future Content combined with and driven by Competencies and Context Maximizing the use of all media and content types Mashups Mass Contribution Mass Customization Personalization Automation of assembly based on demand signal of a performance objective
- 37. Long Tail Marketof one is the biggest of all? The “getting small” of business: Combine enough non hits on the Long Tail and you've got a market bigger than the hits! "The biggest money is in the smallest sales.“ Barnes & Noble carries 130,000 books More than half Amazon sales from OUTSIDE top 130,000 The market for books not sold in the bookstore is larger than those that are! Google makes most of its money from small advertisers eBay the same Children today will grow up never knowing the meaning of “out of print” Stuck in the physical world for our frame of reference Transform from mass markets to mass marketing Finding vs. searching Collaborative filtering, social recommender systems, pattern recognition Say it again…………
- 38. Anatomy of theLong Tail Courtesy Wired magazine
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- 40. STANDARDS Thanks toMs. Shu-jiun (Sophy) Chen for the previous coverage
- 41. Coming Next: CORDRA!C ontent O bject R epository D ISCOVERY and R ESOLUTION A rchitecture See www.cordra.org for MUCH MUCH more!
- 42. CORDRA “Triangle” DiscoveryDelivery Context Identification Location Resolution Retrieval See www.cordra.org for details and MUCH more!
- 43. Content Object ModelE nabling L earning O bject T erminal L earning O bject CONTENT ASSETS Principle Fact Process Overview Procedure Text Audio Summary Concept Principle Process Concept Procedure Fact Overview Summary Objective “ Raw” Data & Media Elements Information Blocks Collections ( Courses , Stories,) Animation Simulation illustration Objective Theme Enabling Objective Terminal Objective Common Content Application Specific Profiles Typical SCO Circa 2000 Typical SCO Circa 2003 Typical SCO Circa 2005 Typical SCO ?Circa 2010? SCO = Shareable Content Object: Lowest level of granularity of learning resources tracked by an LMS using the SCORM Run-Time Environment. The Incredible Shrinking SCO Path to Content Independence* Repurposed with Permission: W.Hodgins ©2004 Learnativity
- 44. IN CLOSING: Everythingbecoming a “mashup” Software People (via competencies) Content events Expand your scope: Metadata for every noun (and some verbs) Competencies & Context LO’s as the model for all/most content not “just” learning Automate everything possible! Massive Scaleablity is mandatory Compounding ROI, putting existing metadata to work Learning from others: eg music, Pandora Consider the “genome” approach different than taxonomies and ontologies?
- 45. Innovation not replication!Let’s stop “flapping” and replicating past artifacts It is NOT about FLAPPING faster!! Let’s start thinking DIFFERENTLY !! How does this apply to YOU? Practice Leadership by EXAMPLE!
- 46. 谢 谢 各位 Thank you very much! For Questions & Comments please contact: [email_address] For slides, podcasts and blogs go to: www.autodesk.com/waynehodgins
- 47.