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Learning Object Metadata

Learning Object Metadata. Masoud Makrehchi PAMI University of Waterloo August 2004. Examples. Multimedia Educational Resource for Learning and Online Teaching- MERLOT EdNa Campus Alberta Repository of Educational Objects- CAREO

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Learning Object Metadata

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  1. Learning Object Metadata Masoud Makrehchi PAMI University of Waterloo August 2004 Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  9. Metadata Subject ------------------------------ Content Creator ------------------------------ Contact Info ------------------------------ Availability ------------------------------ Target Audience ------------------------------ Title ------------------------------ Description ------------------------------ Keyword Learning Object Metadata More Subjective parts of Metadata Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  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). Masoud Makrehchi, PAMI, UW

  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) Masoud Makrehchi, PAMI, UW

  14. Learning Object Metadata Source: www.Schoolnet.Ca http://www.schoolnet.ca/home/e/resources/metadata/newurl_business_education_5861_e.html Masoud Makrehchi, PAMI, UW

  15. Learning Object Metadata http://www.ischool.washington.edu/sasutton/IEEE1484.html Masoud Makrehchi, PAMI, UW

  16. Learning Object Metadata Masoud Makrehchi, PAMI, UW Source: Reusable Learning Objects: Survey of LOM-Based repositories, F. Neven, E. Duval

  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 Masoud Makrehchi, PAMI, UW

  18. Research on Metadata Information Retrieval • 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 Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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) Masoud Makrehchi, PAMI, UW

  21. Case Study Canada’s SchoolNet Masoud Makrehchi, PAMI, UW

  22. Thank you! Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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) Masoud Makrehchi, PAMI, UW

  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 Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

  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) Masoud Makrehchi, PAMI, UW

  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. Masoud Makrehchi, PAMI, UW

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