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

Knowledge Repositories

Wiki

IEEE, ACM,…

Web sites

Libraries

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.

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.

Intellectual Knowledge Discovery

Knowledge Repositories

Wiki

IEEE, ACM,…

Web sites

Libraries

My Model: Intelligent Learning Environment

Interconnecting Knowledge

Neighborhoods

Interconnecting Knowledge

NeighborhoodsAutomatic

Learning Object Aggregation

Automatic Learning Object

Aggregation

PersonalizationPersonalization

AdaptabilityKnowledge Navigator

Knowledge Navigator

Collective IntelligenceCollective

Intelligence

ContextualizationContextualization

Intelligent Learning

Environment

Knowledge GenerationKnowledge Generation

E-learning Platforms

M-learning Platforms Pervasive-learning Platforms

Learners(Human) Devices Agents

A Web of Knowledg

e

A Web of Knowledg

e

Semantic learning Platforms

Learners:

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.

Learning Objects

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

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

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

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

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

LOM Meta Data Example

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.

LO are considered as a lecture or media,… They can not aggregate to make a personalized

lesson Indeed, the major issue is:

Granularity !!

Granular Computing

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

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

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.

PROPOSED SOLUTION

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

Discovery Learning Path 2nd level granulation (Rough-based approach)

LORDLORD

LOLOLearner

PublishDiscovery

Retrieve

LODL

Learning Path

Proposed Model- Reusable Learning Objects

Text

GranulateAnnotate

Feature Extraction Functional Representation of granules

Design TimeRun Time

Publish

Build HierarchicalStructure Of Granules

Publish LODL (Learning Object Description

Language)

Metadata on Text

Metadata on Text

LORD(Learning Object Repository and Directory

Build

Discovery

Rough set Granulation

Learning Path

Customized Lesson

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

LODL

LODL: Learning Object Description Language