1. Semantic Learning Instructor: Professor Cercone Razieh Niazi

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 Knowledge Repositories Wiki IEEE, ACM,… Web sites Libraries

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 Knowledge Repositories Wiki IEEE, ACM,… Web sites Libraries

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

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  1 st 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  2 nd level granulation (Rough-based approach) LORD LO Learner Publish Discovery Retrieve LODL Learning Path

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

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

27. LODL  LODL: Learning Object Description Language