The Future of Metadata & Learning Objects Towards a more holistic perspective on learning and performance International Conference on Digital Archive Technology Taipei, Taiwan Oct. 19, 2006

[email_address] Strategic Futurist President & Co-Founder Learnativity.org Chair, IEEE Learning Technology Standards Committee Learning Object Metadata 0 Strategic Advisor

www.creativecommons.org

Grand Vision In a 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.”

In a 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.”

Maybe Goldilocks had it 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

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 ………

“ C -ing the Future” C ontent, C ompetencies C ontext LEARNING & PERFORMANCE CONTENT CONTEXT COMPETENCE

“ C-ing” the Future 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.

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.

It all depends upon…………….. METADATA CONTENT CONTEXT COMPETENCE

Metadata: The past 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,

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,

Metadata: The Present 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

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

networking, not taking over repositories Slide courtesy Erik Duval, KU Leuven

Federated Metadata Slide courtesy Erik Duval, KU Leuven

Metadata: Current Challenges 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

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

Metadata: beyond data All the nouns; people, places and things People: Competencies skills, knowledge, abilities, Location Eg. GIS, 3D info such as height Merges into context Context

All the nouns; people, places and things

People:

Competencies

skills, knowledge, abilities, Location

Eg. GIS, 3D info such as height

Merges into context

Context

Metadata: The Future 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

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

Metadata: The Future 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

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

Visualizing reuse http://ariadne.cs.kuleuven.be/infovis/ Slide courtesy Erik Duval, KU Leuven

Federated AMG Engine http://ariadne.cs.kuleuven.be/amg/ Slide courtesy Erik Duval, KU Leuven

Attention Metadata http://ariadne.cs.kuleuven.ac.be/empirical/ Slide courtesy Erik Duval, KU Leuven

User feedback Slide courtesy Erik Duval, KU Leuven

Contextual and Automated Metadata Generation (AMG) http://ariadne.cs.kuleuven.be/amg/ Slide courtesy Erik Duval, KU Leuven

scaleable remix Slide courtesy Erik Duval, KU Leuven

Hiding everything but the benefits http://ariadne.cs.kuleuven.be/alocom/ Slide courtesy Erik Duval, KU Leuven

A glimpse of the 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”??

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”??

Pandora and the Music Genome Project

“ Individualized” Career Development 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

LEARNING OBJECTS The world of CONTENT

The world of CONTENT

Learning Objects: The Past Learning Objects initially proposed in 1992 with LALO A Lego model of modularity

Learning Objects initially proposed in 1992 with LALO

A Lego model of modularity

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

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

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

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

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

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

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

SkillObject ™ 0

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

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

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

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

Long Tail Market of 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…………

Market of 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…………

Anatomy of the Long Tail Courtesy Wired magazine

Courtesy Wired magazine

STANDARDS Thanks to Ms. Shu-jiun (Sophy) Chen for the previous coverage

Coming Next: CORDRA! C ontent O bject R epository D ISCOVERY and R ESOLUTION A rchitecture See www.cordra.org for MUCH MUCH more!

C ontent

O bject

R epository

D ISCOVERY and

R ESOLUTION

A rchitecture

CORDRA “Triangle” Discovery Delivery Context Identification Location Resolution Retrieval See www.cordra.org for details and MUCH more!

Content Object Model E 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

IN CLOSING: Everything becoming 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?

Everything becoming 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?

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!

Let’s stop “flapping”

and replicating past artifacts

It is NOT about

FLAPPING faster!!

Let’s start thinking

DIFFERENTLY !!

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