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  • 1. Learning Object Metadata Masoud Makrehchi PAMI University of Waterloo August 2004
  • 2. Examples
    • Multimedia Educational Resource for Learning and Online Teaching- MERLOT
    • EdNa
    • Campus Alberta Repository of Educational Objects- CAREO
    • eduSource Canada- a network for learning objects repositories
  • 3. Learning Objects
    • Learning Objects can be defined as any digital resource and associated metadata, which can be used, re-used or referenced during technology support learning.
    Learning Object Content Object Metadata
  • 4. Learning Objects
    • Learning objects
      • also known as
        • digital objects
        • knowledge objects
        • educational objects
        • instructional objects
        • intelligent objects
        • reusable learning objects
        • data objects
      • including small, independent chunks of digital information that can be reused in their original form or adapted to meet the needs of unique learners.
  • 5. Learning Objects
    • The content of a learning object can include
      • image
      • interactive game
      • assessment
      • digital video
      • multi-media file
      • instructional text
      • web site
      • sound file
      • simulation
  • 6. Learning Objects
    • Benefits of Using Learning Objects
      • personalized learning
      • increased selection of learning material
      • reduced development time
      • reuse of resources
    • Motivations
      • Multicultural and multilingual societies (Canada, Austria, EU, USA and China)
      • Long distances and expensive educational cost
  • 7. Metadata
    • Metadata is data about data. Metadata is information that describes content.
    • Descriptive metadata is stored in a database.
      • Information such as the title, author, producer, date of production, and a description of the content are just a few examples of metadata that is normally stored in the database.
    • Metadata can be entered manually or it can be generated automatically .
  • 8. Metadata
    • Objective Metadata
      • are factual data, most of which can be generated automatically – things such as physical attributes, date, author, operational requirements, costs, identification numbers, and ownership.
    • Subjective Metadata
      • the more varied and valuable attributes of a learning object determined by the person or group who creates the metadata, such as subject, category, and discription.
  • 9. Metadata Learning Object Metadata Subject ------------------------------ Content Creator ------------------------------ Contact Info ------------------------------ Availability ------------------------------ Target Audience ------------------------------ Title ------------------------------ Description ------------------------------ Keyword More Subjective parts of Metadata
  • 10. Learning Object Metadata
    • In web based learning, the trend is to encode learning materials with meaningful and machine understandable metadata in order to facilitate modular and reusable content repositories.
    • Learning object metadata is usually represented in XML or RDF format.
  • 11. Learning Object Metadata
    • In learning object repositories, Metadata automatically retrieved, filtered by learning object repositories but metadata is not automatically generated.
    • Metadata is used not only in searching and access to the learning object repositories but also in reusing learning object materials and learning objects aggregation.
    • Learning object metadata is the base of most operations on learning objects.
  • 12. Learning Object Metadata
    • Learning object repository stores both learning objects and their metadata in two different ways
      • Storing them physically together (CLOE)
      • Learning Objects and their metadata stored separately (SchollNet and MERLOT)
    • Most Learning Object Repositories are actually learning object metadata repository in which every metadata includes the link to the learning object resource (content is somewhere else).
  • 13. Learning Object Metadata Standards
    • Instructional Management Systems Project (IMS)
    • Advanced Distributed Learning Initiative (ADL) and SCORM
    • Alliance of Remote Instructional Authoring and Distribution Networks for Europe (ARIADNE)
    • Dublin Core Metadata Initiative
    • IEEE Learning Technology Standards Committee (LTSC) Learning Object Metadata- IEEE 1484
    • Canadian Core Learning Object Metadata (CanCore)
    • World Wide Web Consortium (W3C)
  • 14. Learning Object Metadata Source: www.Schoolnet.Ca http://www.schoolnet.ca/home/e/resources/metadata/newurl_business_education_5861_e.html
  • 15. Learning Object Metadata http://www.ischool.washington.edu/sasutton/IEEE1484.html
  • 16. Learning Object Metadata Source: Reusable Learning Objects: Survey of LOM-Based repositories, F. Neven, E. Duval N/A 48 All IEEE LOM (SCORM) USA Lydia N/A 15782 Education Dublin Core Australia Edna N/A N/A K-12 IEEE LOM (CanCore) Canada Learn-Alberta N/A 1576  4042 All IEEE LOM (CanCore) Canada CAREO N/A N/A Health Science IEEE LOM (CanCore) USA HEAL N/A 7408 All IEEE LOM USA MERLOT N/A 880 Science and Engineering IEEE LOM USA iLumina N/A 170 Science and Engineering IEEE LOM USA Learning Matrix N/A 1645 Science and Engineering IEEE LOM USA SMETE In progress 2498 All IEEE LOM Europe ARIADNE Automatic Metadata Number of LOs Subject Domain Metadata Schema Country
  • 17. Research on Metadata
    • The purpose of using Metadata
      • Access and usability of the information resource (a book, a web page, a learning object, or even a service)  learner, …
      • Information Management, categorization, information integration and aggregation, reusability  administrators and developers
  • 18. Research on Metadata
    • The purpose of using Metadata
      • Access and usability of the information resource (a book, a web page, a learning object, or even a service)  learner, …
      • Information Management, categorization, information integration and aggregation, reusability  administrators and developers
    Data Mining and Machine Learning Information Retrieval
  • 19. Case Study
    • We need data to develop machine learning and data mining techniques for LORNET.
    • Learning object metadata data set
      • metadata + content object (raw data)
      • Preferably Labelled
      • We know gathering content data and converting to text can be difficult or impossible (assume, a learning object can be just a java applet!)  we have to work only with metadata
  • 20. Case Study
    • Canada’s SchoolNet
      • Most learning resources are not actual learning object
      • Contains a huge number of metadata, mostly informative.
      • More than 7000 learning resources in 17 categories (labeled metadata)
  • 21. Canada’s SchoolNet Case Study
  • 22. Thank you!
  • 23. Research Motivation
    • Automatic generation of a number of metadata fields to facilitate the generating metadata repository.
      • In ARIADNE (an European-based Learning Object initiative) project, working on the area of automatic metadata generation is currently in progress.
  • 24. Proposed Schema
    • Since metadata includes many objective and subjective parts, then in the proposed research we focus on only most important subjective parts (except Description part which is more challenging);
      • Subject/Category
      • Keywords
  • 25. Metadata Subject Extraction
    • Since LOR data is usually in form of web data (HTML or XML), then we can use tag information in document representation and feature selection
    • Document representation
      • Document Vector and/or Ontology
    • Dimensionality reduction (feature selection)
      • Information theoretic approach
      • Latent semantic indexing (SVD)
    • Classification (supervised learning)
      • Soft computing approach (fuzzy classification rules)
  • 26. Metadata Keywords Extraction
    • Proposed algorithm
      • Term clustering in every LO data vector
      • Finding the optimum association between these clusters and keywords in Metadata vector through an optimization process (for example a Genetic Algorithm)
      • Extracting association rules
  • 27. Information Requirements
    • To train and test the proposed schema, we need a plenty of learning object data with their Metadata,
      • Learning Object data in text or HTML is preferred.
      • Metadata is usually presented in RDF or XML format, we prefer these kind of metadata.
  • 28. Metadata
    • Defines attributes for characteristic about each content object used in authoring of learning objects (i.e. Title, description, author, etc.).
    • “ It facilitates searching, management and linking granules of content. Allows users and authors of content to search, retrieve and assemble content objects according to parameters defined by users” (Hodgins, etal)
  • 29. Learning Object Metadata
    • Metadata allows people to search the repository for content.
    • To support flexible access to the LO’s
      • an efficient search and retrieval system is required.
      • LO metadata capture characteristics of LO’s and their educational information.