Scott Bateman, Christopher Brooks, Gordon McCalla, Jim Greer

1. Learning Object MetadataFrom the locally prescribed to the socially derived(or, a look back at 4 years of LORNET at the University of Saskatchewan Scott Bateman, Christopher Brooks, Gordon McCalla, Jim Greer Advanced Research in Intelligent Educational Systems Department of Computer Science University of Saskatchewan, Canada

2. In the beginning... The world of Learning Objects was still in early definitional stages The focus was on metaphors of LEGO, atoms, organic structures... But metaphors can be quite limiting They tend to limit your vision Are learning objects just like physical structures? Not really... Metaphors ignored the issue of metadata, and focused on the “physical” connections between learning objects

3. In the beginning.. Metadata was simple Text only, mostly following the Dublin Core specification of authorship, subject, and keywords No ability to describe intended usage Really, there are two kinds of intended usage how the system should use the learning objects and how the users did use the learning objects Metadata was generated by the authors of the content directly, and was laboursome to create due to lack of tools Instructors still needed to manually find and aggregate resources into courses...

4. Contributions to LO Metadata Models for learning object metadata that make use of AI techniques Ontologies for describing what students have done (cf. LOCO Analyst by Jovanovic et al.)‏ Ontologies for describing how the domain connects to learning High level approaches for metadata creation Agile User Modelling (Vassileva et al)‏ Ecological Approach (McCalla)‏ Flexible Learning Object Metadata (Brooks & McCalla)‏

5. Specific Systems iHelp Courses Is able to provide simple sequencing adaptivity based on end-user data Provides a methods for determining what students have done Provides privacy for the protection of users when data mining and visualization activities occur MUMS Architecture of a distributed user modelling system, providing SOAP-based subscription access to RDF knowledge Knowledge can be traditional metadata, or metadata fitting new schemas

6. Specific Systems Open Annotation and Tagging System (OATS)‏ Social construction of learning object metadata through collaborative tagging Leverage the “note taking” approach of students Recollect Video course casting system Metadata created through data mining slide images Variety of techniques used, including conversion of images to textual data, pixel and block characteristics, etc.

7. Questions As usual, we are left with more questions than answers Is metadata a collection of information, or a process of deciding over the usage knowledge that has been collected? Is automated metadata generation from content artifacts a reasonable approach? Experiments suggest that students, instructors, and automatic metadata generation tools all classify content differently (Bateman et al., WWW 07)‏ Where did all the agents go? Metadata is still just human text, or very limited vocabularies...‏

8. Important Directions Humans give us a very important kind of metadata just by using our systems Content metadata, such as in collaborative tagging systems, is great for exposing to end users who are looking for informationAttention Metadata Pivot browsing, association creation, and categorization using collective intelligence Usage metadata (Attention Metadata) helps decide how to change things automatically Automatic capture of learner interaction data, possibly mined and interpreted and attached to learning objects Content adaptation, recommendation, etc. Learning environments need to be smart, and share how people interact

9. Thank You • Thank to our many collaborators on this work • This work was funded principally by NSERC through the LORNET Network • ai.usask.ca