1. Advanced Distributed Learning

2. Conditions Before SCORM  Couldn’t move courses from one Learning Management System to another  Couldn’t reuse content pieces across different courses  Couldn’t sequence reusable content for branching, remediation and other tailored learning strategies  Couldn’t search learning content libraries or media repositories across different LMS environments

3. Lab Safety Sharable Content Using the Battery Lab Chemical Engineering Common Reagents & Safety Issues Microbiology Each course uses the same module on eyewash procedure, because the content is sharable and reusable General Chemistry Sharable Content Object

4. Granularity and Reusability Raw Data (Media Elements) Information Objects Learning Objective Lesson (Aggregation) Course (Collection) Source: Academic ADL Co-Lab (adapted from Learnativity) Reusability Context

5. Proprietary Solutions Proprietary solutions may work fine as long as you can stay with the same system. Content stops working when you try to migrate to other systems

6. SCORM Adoption  US Department of Defense (DoD)  Government Agencies  IRS, CDC, DoL, NGB, NSA, USPS, TSA, VA, NASA, TSWG, others  IRS, CDC, DoL, NGB, NSA, USPS, TSA, VA, NASA, TSWG, others  Industry  Daimler Chrysler, IBM, Microsoft, Boeing, LG, Verizon, Delta Airlines, Oracle, Cisco, McDonalds, Home Depot, others  International  Australia, Canada, Asia, Europe, Latin America

7. Semantic Web Provides automated information access based on machine-processable semantics of data and heuristics that use these metadata. Provides automated information access based on machine-processable semantics of data and heuristics that use these metadata. The explicit representation of the semantics of data, accompanied with domain theories (that is, ontologies), will enable a Web that provides a qualitatively new level of service. The explicit representation of the semantics of data, accompanied with domain theories (that is, ontologies), will enable a Web that provides a qualitatively new level of service.

8. Semantic Web It will weave together an incredibly large network of human knowledge and will complement it with machine processability. Various automated services will help the user to achieve goals by accessing and providing information in a machine-understandable form. It will weave together an incredibly large network of human knowledge and will complement it with machine processability. Various automated services will help the user to achieve goals by accessing and providing information in a machine-understandable form. This gives us a completely new perspective of the knowledge acquisition and engineering and the knowledge representation communities. This gives us a completely new perspective of the knowledge acquisition and engineering and the knowledge representation communities.

9. Knowledge management KM is concerned with acquiring, maintaining, and accessing an organization’s knowledge. Its purpose is to exploit an organization’s intellectual assets for greater productivity, new value, and increased competitiveness. KM is concerned with acquiring, maintaining, and accessing an organization’s knowledge. Its purpose is to exploit an organization’s intellectual assets for greater productivity, new value, and increased competitiveness. With the large number of online documents, several document management systems have entered the market. However, these systems have weaknesses. With the large number of online documents, several document management systems have entered the market. However, these systems have weaknesses.

10. Weaknesses Searching information: Existing key word-based searches retrieve irrelevant information that uses a certain word in a different context; they might miss information when different words about the desired content are used.

11. Weaknesses Extracting information Current human browsing and reading requires extracting relevant information from information sources. Automatic agents lack the commonsense knowledge required to extract such information from textual representations, and they fail to integrate information spread over different sources.

12. Weaknesses Maintaining: Sustaining weakly structured text sources is difficult and time consuming when such sources become large. Keeping such collections consistent, correct, and up to date requires a mechanized representation of semantics and constraints that can help detect anomalies.

13. Weaknesses Automatic document generation: Adaptive Web sites that enable dynamic reconfiguration according to user profiles or other relevant aspects could prove very useful. The generation of semi-structured information presentations from semi- structured data requires a machine- accessible representation of the semantics of these information sources.

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

19. http://conceptshare.com/tour/ http://conceptshare.com/tour/ http://conceptshare.com/tour/ http://eportfolio.stanford.edu/ConceptMap. aspx?phoid=1614 http://eportfolio.stanford.edu/ConceptMap. aspx?phoid=1614 http://eportfolio.stanford.edu/ConceptMap. aspx?phoid=1614 http://eportfolio.stanford.edu/ConceptMap. aspx?phoid=1614

24. What’s Next? The future of WBL industry depends on you!!!! The future of WBL industry depends on you!!!!