Informal Knowledge In E Learning

Adapting informal sources of knowledge to e-Learning Jacek Jankowski, Jaroslaw Dobrzanski, Filip Czaja Digital Enterprise Research Institute National University of Ireland, Galway <firstname.lastname> @deri.org

Presentation scope

Motivation

Formal and Informal learning

Didaskon project

IKHarvester project

Social Semantic Information Sources (SSIS )

Conclusions

Motivation

Huge amount of information to capture

Predefined, rigid courses – made once and for all

Expensive content creation and maintenance

80% of possessed knowledge is acquired from informal sources

Formal and Informal learning

Formal learning:

Traditional, old, preparatory approach (i.e. gathering in a classroom)

Predefined, inflexible courses – made once and for all

Training is PUSHED

Employs advanced and expensive solutions (LMS)

Informal learning:

More natural, unofficial aproach

Flexible and spontaneous – learn when/where/what you want

Learning is PULLED

Free in most cases

Didaskon

Didaskon - a framework for automated composition of a learning path for a student

Architecture of the future e-Learning system (our idea presented on LACLO 2006):

Ontology for user model – delivering personalised content

Ontology for content - ensuring cooperation of heterogeneous environments which use different formats

Didaskon Context

Didaskon - Architecture

Didaskon – e-Learning framework, that will be based on existing solutions:

Users management: FOAFRealm, Windows CardSpace

Formal Repositories (for learning object’s): LOstRepository

Informal Repository: IKHarvester

MarcOnt – handling different formats

UDDI – Didaskon API description

IKHarvester - Informal Knowledge Harvester

Social Semantic Information Sources (SSIS)

Compilation of the Semantic Web and Web 2.0

Collaboration

Sharing

Semantic annotations for resources

Interlinking resources and people related to the m

Dedicated for people and computers

Examples:

Semantic wikis: Semantic MediaWiki extension

Semantic blogs: SIOC Plugin for WordPress

JeromeDL – the Social Semantic Digital Library

IKHarvester - Goals

Capturing informal learning/knowledge from SSIS

Providing data for eLearning frameworks, e.g. Didaskon

Data Harvesting

The Semantic Web

RDF feeds (semantic wikis)

Relation with RDF documents

Information in HTML

Non-semantic web pages

HTML of Wikipedia or blogs on Blogger still is quite semantic – common templates of web pages

HTML scraping

Data Providing

Learning Object Metadata (LOM)

Standard underlying SCORM 2004

LOM features:

Used in a number of LMSs

Rich description

Many aspects: educational, technical, relations with other LOs, classification, ...

IKHarvester - Architecture

Service Oriented Architecture ensures:

Encapsulation

Abstraction – hidden logic

Loose coupling - independancy

Quicker reposnses

Reusability - one deployment, many usages

REST-based Web Services

Popular with Web 2.0 and the Semantic Web

Resource-oriented

IKHarvester – API specification Removes LOM for a specified LO DELETE http://server/ikh/soa/$URI$ Adds/updates LOM for a specified LO PUT / POST http://server/ikh/soa/$URI$ Returns the content of a specified LO GET http://server/ikh/soa/$URI$/content Returns LOM for a specified LO GET http://server/ikh/soa/$URI$/manifest Returns available LOs or LOs of the specified type ( type parameter) GET http://server/ikh/soa/[type] Description HTTP Method URL

Extensibility – support for new types of resources

Comparison with existing tools

Conclusions