Learning Objects on the Semantic Web

1. Learning Objects on the Semantic Web Permanand Mohan Department of Mathematic and Computer Science University of the West Indies St. Augustine, Trinidad and Tobago Christopher Brooks Advanced Research in Intelligent Educational Systems (ARIES) Laboratory Computer Science Department University of Saskatchewan Saskatoon, Saskatchewan, Canada

2. Adaptive Learning • Adaptive learning is one of the primary goals of e-learning • Dynamic just-in-time generation of learning resources based on the beliefs, desires, and goals of a student to facilitate learning • There are three main stakeholdersin adaptive learning

3. What we have now • Currently there exists work for: • Sequencing content together (instructional design) • Identifying pedagogical aspects of content (instructional design/library systems) • Cataloging content for discovery (library systems) • Repositories for searching content catalogs (library systems/computer science) • Identifying the beliefs, desires, and goals of a learner (computer science) • Correlating these beliefs, desires, and goals with appropriate educational content (computer science/instructional design)

4. So, do we have adaptive learning?

5. Learning Object • The focus of adaptive learning is now starting to shift the “glue” of all of these areas, termed the learning object • Norm Friesen identifies three properties that are important: • Discoverability • Modularity • Interoperability • But, most implementations have been decidedly simple: • Usually just static HTML web tutorials • Some java applets with limited ability to test a user then adapt content [EOE] • Large search engine like repositories for users to find content [CAREO, MERLOT, etc]

6. The need for Vocabularies • Biggest challenge facing learning object adoption at large: Syntactic interoperability • There is no commonly agreed upon format for learning objects (HTML, XML, image formats, applets, …) • How can a LCMS know how a learning object can be displayed, and if it worked well for a user? [CISCO 2001]

7. Object Oriented Learning Objects • We feel that object oriented principles, though hotly debated [see Sosteric & Hesemeier], are ripe to help • Content should have interfaces which describes what abilities or situations the content can be used in (e.g. [Wiley]’s learning object types) • Content should be able to be queried (method/function support) so Learning Content Management Systems (LCMS) can manipulate the content allowing for encapsulation • Content should support inheritance to allow for easily deriving new content (see for instance, [Wu]) • Instructional design patterns can be formalized and applied to build objects that use proven methods of conveying meaning

8. Implementation • A number of web based implementation options are being explored to achieve this: • Content and metadata stored with XML • Object interfaces exported with WSDL and interacted with using SOAP • Object methods implemented with XSLT, relying on predefined functions within compliant LCMSs • XML is nice to use for this: • Widescale support (many free tools) • Vendor neutral format improves interoperability

9. Metadata • Distribution of metadata: • Centralized (e.g. CAREO, MERLOT) • Decentralized (e.g. POOL, edutella) • The majority (all?) of implementations completely decouple metadata from content • This is an antiquated “library & book” view of learning objects • Really two kinds of metadata • Tightly coupled: Objective or “derived” metadata to be stored with the learning object itself • Loosely coupled: Opinions about the learning object’s fitness for purpose made by members in the community

10. Two kinds of Metadata • Tightly Coupled Metadata • E.g. Language, digital rights, versioning information, etc. • Usually more technical information • Can be Implemented as a method within a learning object (WSDL/SOAP) which returns a set of values (XML Document) corresponding to a given type (XML Schema) • Loosely Coupled Metadata • E.g. Semantic density, interactivity type, thinking styles, etc. • Usually more pedagogical information • RDF is a key technology • Everyone has an opinion, RDF lets us express this

11. Future Work: Making learning objects smarter • Object principles are a good start, but future work involves experimenting with higher level reasoning as well as autonomy (agent characteristics) • We envision these principles to be especially useful in the area of discovery • Intelligent learning objects being aware of one another and of users in the LCMS can better advertise their services

12. Future Work: LORNET • Learning Object Repository Network project • Involves 5 Canadian universities • Focuses on the intersection of knowledge management, the semantic web, and e-learning • Our goals in particular • Integrating user modeling to help provide adaptive content retrieval • Providing a flexible platform which takes advantage of the distributed nature of learning objects • Providing learning object retrieval “just-in-time” instead of using a precompiled approach

13. Future Work: Agents are key • Student modeling plays a big part of our vision • As objects interact with students, they learn more about what situations they are good for • Learning objects can then negotiate with one another to determine which is most appropriate for a given situation • Hopefully leads to “emergent courses”, where a path through a course can be better chosen by examining the characteristics of a student and comparing them to successes found for similar students

14. Conclusion: Final Words • Learning objects are meant to act as the “glue” between the areas of instructional design, library sciences, and computer science as they try to achieve adaptive learning • An object oriented approach to content creation is especially useful for creating more modular and interoperable learning resources • XML technologies, specifically XML, WSDL, SOAP, and XSLT are well poised to achieve object oriented learning objects in a vendor-neutral manner

15. Questions? Contact information: http://www.cs.usask.ca/research/research_groups/aries/ Permanand Mohan: pmohan@tstt.net.tt Christopher Brooks: cab938@mail.usask.ca www.cs.usask.ca/~cab938