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Semantic Learning

Semantic Learning. Instructor: Professor Cercone. Razieh Niazi. Outline. Introduction Issues in the Current State of Knowledge Discovery Intellectual Knowledge Discovery Learning Objects Granularity Issue Proposed Solution. Introduction. Current State of Knowledge Discovery. Libraries.

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Semantic Learning

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  1. Semantic Learning Instructor: Professor Cercone RaziehNiazi

  2. Outline • Introduction • Issues in the Current State of Knowledge Discovery • Intellectual Knowledge Discovery • Learning Objects • Granularity Issue • Proposed Solution

  3. Introduction

  4. Current State of Knowledge Discovery Libraries Wiki Web sites Knowledge Repositories IEEE, ACM,…

  5. Problems in the current state • Knowledge discovery: difficult • Information overload: • Most of the problems which we have finding a path though the huge amount of information currently available not only on the Web but in books, newspapers, television and films. • Evaluating these information needs skills • Information authentication • Indexing facilities are used in conventional systems like libraries, and in search engines. sites exist which present themselves as impartial research conduits when in fact they are funded by commercial and other interests.

  6. Knowledge neighborhoods • Customization of information discovery: • Given the amount of information available, the problem of matching learner to material, which is relevant to his or her needs at a particular point in time, becomes more and more required.

  7. Intellectual Knowledge Discovery Libraries Wiki Web sites Knowledge Repositories IEEE, ACM,…

  8. My Model: Intelligent Learning Environment A Web of Knowledge Interconnecting Knowledge Neighborhoods Automatic Learning Object Aggregation Collective Intelligence Intelligent Learning Environment Contextualization Personalization Adaptability Knowledge Navigator Knowledge Generation E-learning Platforms Pervasive-learning Platforms M-learning Platforms Semantic learning Platforms Learners: Learners(Human) Devices Agents

  9. Dream comes True!!! • Basic components: • Annotated educational resources, • a means of reasoning about these, • and a range of associated services. • The basic step is having ability to aggregate learning object.

  10. Learning Objects • What is a Learning Object? • small units of learning resources • self-contained • are reused • are aggregated, and combined

  11. Reusable Learning Object • "reuse" means placing a learning object in a context other than that for which it was designed • What “Reusable Learning Object” brings for us? • Personalized Learning • Customized Lessons • Interconnecting Knowledge Neighborhoods • Generate Knowldge

  12. Current State of Learning Object • Learning objects are identified with metadata so that they can be referenced and searched both by authors and learners. Cisco Model

  13. Scorm • SCORM stands for Sharable Content Object Reference Model, initiated by Advanced Distributed Learning (ADL) specification group. • Issues: • the current design of SCORM has resulted in: • the slow pace • high cost developing of learning objects • not able to be tailored to individual needs

  14. LOM • LOM: IEEE Learning Object Metadata • Learning Object Metadata is a data model encoded in XML and used to describe learning objects. • Developed by IEEE supports reusability of learning objects, aids discoverability and facilitates interoperability in the context of online learning management systems

  15. LOM Meta Data Example

  16. Issues with the Current State • A concept can be described by two dimensions including: • Intention: • Set of concept’s attribute and values • Extention: • A set of objects that belongs to the concept • The current metadata standards provide the extension of the objects.

  17. LO are considered as a lecture or media,… • They can not aggregate to make a personalized lesson • Indeed, the major issue is: Granularity !!

  18. Granular Computing

  19. In the philosophical perspective: • Granular computing attempts to extract and formalize human thinking. • In the methodological perspective: • It concerns structured problem solving. • In the computational perspective: • It is a paradigm of structured information processing. It addresses the problems of information processing in the abstract

  20. Granular computing exploits structures in terms of granules, levels, and hierarchies based on multilevel and multi-view representations • A granule normally consists of elements that are drawn together by indistinguishability, similarity or functionality

  21. Writing may be viewed as a problem solving process and task. • A simple idea is described by a paragraph consisting of several sentences. • A point-of-view is jointly described and supported by several ideas.

  22. Proposed Solution

  23. Tasks • Building Granular learning objects: • Annotation • Metadata based on standards i.e: IMS • 1st level Granulation • Feature Extraction • Functional Representation of Granules • Hierarchical Structure Of Granules • Description language for Learning Objects • Publish • Universal Repository for published learning objects

  24. Discovery • Learning Path • 2nd level granulation (Rough-based approach) LORD Publish Discovery LODL Learner LO Retrieve Learning Path

  25. Proposed Model- Reusable Learning Objects Build Functional Representation of granules Feature Extraction Text Build Hierarchical Structure Of Granules Annotate Granulate Publish LODL (Learning Object Description Language) Metadata on Text Publish LORD(Learning Object Repository and Directory Discovery Rough set Granulation Learning Path Customized Lesson Design Time Run Time

  26. Proposed Model: Functional Representation of the Learning Objects Endpint: http://www.fuzzy-logic.com/Ch1.htm Endpoint: https://wiki.cse.yorku.ca/course_archive/2010-11/W/4403/lectures http://www.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq-doc-2.html

  27. LODL • LODL: Learning Object Description Language

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