Advanced Distributed Advanced Distributed LearningLearning

Conditions Before SCORMConditions Before SCORM

Couldn’t move courses from one Learning Couldn’t move courses from one Learning Management System to anotherManagement System to another

Couldn’t reuse content pieces across Couldn’t reuse content pieces across different coursesdifferent courses

Couldn’t sequence reusable content for Couldn’t sequence reusable content for branching, remediation and other tailored branching, remediation and other tailored learning strategieslearning strategies

Couldn’t search learning content libraries or Couldn’t search learning content libraries or media repositories across different LMS media repositories across different LMS environmentsenvironments

Lab SafetyLab Safety

Sharable ContentSharable Content

Using the Using the Battery LabBattery Lab

Chemical EngineeringChemical Engineering

Common Common Reagents & Reagents & Safety IssuesSafety Issues

MicrobiologyMicrobiology

Each course uses the same module on eyewash procedure, because the content is sharable and reusable

General ChemistryGeneral Chemistry

Sharable Content ObjectSharable Content Object

Granularity and ReusabilityGranularity and ReusabilityRaw Data

(Media Elements)Information

ObjectsLearning Objective

Lesson(Aggregation)

Course(Collection)

Source: Academic ADL Co-Lab (adapted from Learnativity)

Reusability

Context

Proprietary SolutionsProprietary Solutions

Proprietary solutions may Proprietary solutions may work fine as long as you can work fine as long as you can stay with the same system.stay with the same system.

Content stops working Content stops working when you try to migrate to when you try to migrate to other systems other systems

SCORM AdoptionSCORM Adoption US Department of Defense (DoD)US Department of Defense (DoD)

Government AgenciesGovernment Agencies IRS, CDC, DoL, NGB, NSA, USPS, TSA, IRS, CDC, DoL, NGB, NSA, USPS, TSA,

VA, NASA, TSWG, othersVA, NASA, TSWG, others

IndustryIndustry Daimler Chrysler, IBM, Microsoft, Boeing, Daimler Chrysler, IBM, Microsoft, Boeing,

LG, Verizon, Delta Airlines, Oracle, Cisco, LG, Verizon, Delta Airlines, Oracle, Cisco, McDonalds, Home Depot, othersMcDonalds, Home Depot, others

InternationalInternational Australia, Canada, Asia, Europe, Latin AmericaAustralia, Canada, Asia, Europe, Latin America

Semantic WebSemantic Web

Provides automated information access baProvides automated information access based on machine-processable semantics of sed on machine-processable semantics of data and heuristics that use these metadadata and heuristics that use these metadata.ta.

The explicit representation of the semanticThe explicit representation of the semantics of data, accompanied with domain theoris of data, accompanied with domain theories (that is, es (that is, ontologiesontologies), will enable a Web t), will enable a Web that provides a qualitatively new level of serhat provides a qualitatively new level of service. vice.

Semantic WebSemantic Web

It will weave together an incredibly large network It will weave together an incredibly large network of human knowledge and will complement it with of human knowledge and will complement it with machine processability. Various automated servicmachine processability. Various automated services will help the user to achieve goals by accessines will help the user to achieve goals by accessing and providing information in a machine-understg and providing information in a machine-understandable form. andable form.

This gives us a completely new perspective of thThis gives us a completely new perspective of the knowledge acquisition and engineering and the e knowledge acquisition and engineering and the knowledge representation communities.knowledge representation communities.

Knowledge managementKnowledge management

KM is concerned with acquiring, maintaininKM is concerned with acquiring, maintaining, and accessing an organization’s knowledg, and accessing an organization’s knowledge. Its purpose is to exploit an organizatioge. Its purpose is to exploit an organization’s intellectual assets for greater productivin’s intellectual assets for greater productivity, new value, and increased competitivenety, new value, and increased competitiveness. ss.

With the large number of online documentWith the large number of online documents, several document management systems s, several document management systems have entered the market. However, these shave entered the market. However, these systems have weaknesses.ystems have weaknesses.

WeaknessesWeaknessesSSearching informationearching information: :

Existing key word-based searches retriExisting key word-based searches retrieve irrelevant information that uses a eve irrelevant information that uses a certain word in a different context; thecertain word in a different context; they might miss information when differey might miss information when different words about the desired content arnt words about the desired content are used. e used.

WeaknessesWeaknessesExtracting informationExtracting information

Current human browsing and reading rCurrent human browsing and reading requires extracting relevant information equires extracting relevant information from information sources. Automatic afrom information sources. Automatic agents lack the commonsense knowledgents lack the commonsense knowledge required to extract such informatioge required to extract such information from textual representations, and thn from textual representations, and they fail to integrate information spread ey fail to integrate information spread over different sources.over different sources.

WeaknessesWeaknessesMaintainingMaintaining: : Sustaining weakly structured text sources iSustaining weakly structured text sources is difficult and time consuming when such s difficult and time consuming when such sources become large. Keeping such collesources become large. Keeping such collections consistent, correct, and up to date rctions consistent, correct, and up to date requires a mechanized representation of seequires a mechanized representation of semantics and constraints that can help detemantics and constraints that can help detect anomalies.ct anomalies.

WeaknessesWeaknesses

Automatic document generationAutomatic document generation::

Adaptive Web sites that enable dynamic reAdaptive Web sites that enable dynamic reconfiguration according to user profiles or configuration according to user profiles or other relevant aspects could prove very usother relevant aspects could prove very useful. The generation of semi-structured infoeful. The generation of semi-structured information presentations from semi-structurermation presentations from semi-structured data requires a machine-accessible reprd data requires a machine-accessible representation of the semantics of these inforesentation of the semantics of these information sources.mation sources.

OntologiesOntologies

Using, ontologies semantic annotations will allow Using, ontologies semantic annotations will allow structural and semantic definitions of documentstructural and semantic definitions of documents. These annotations could provide completely ns. These annotations could provide completely new possibilities: intelligent search instead of keyew possibilities: intelligent search instead of keyword matching, query answering instead of inforword matching, query answering instead of information retrieval, document exchange between demation retrieval, document exchange between departments through ontology mappings, and definipartments through ontology mappings, and definitions of views on documents.tions of views on documents.

http://conceptshare.com/tour/http://conceptshare.com/tour/ http://eportfolio.stanford.edu/ConceptMahttp://eportfolio.stanford.edu/ConceptMa

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What’s Next?What’s Next?

The future of WBL industry depends The future of WBL industry depends on you!!!!on you!!!!