Copyright 2007 Digital Enterprise Research Institute. All rights reserved.

www.deri.ie

Adapting informal sources of knowledge to e-Learning

Jacek Jankowski, Jaroslaw Dobrzanski, Filip CzajaDigital Enterprise Research Institute

National University of Ireland, Galway

<firstname.lastname>@deri.org

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Presentation scope

• Motivation

• Formal and Informal learning

• Didaskon project

• IKHarvester project

• Social Semantic Information Sources (SSIS)

• Conclusions

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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

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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

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Didaskon

DidaskonDidaskon - 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

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Didaskon Context

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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

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IKHarvester - Informal Knowledge Harvester

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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 them– Dedicated for people and computers

• Examples:– Semantic wikis: Semantic MediaWiki extension– Semantic blogs: SIOC Plugin for WordPress– JeromeDL – the Social Semantic Digital Library

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IKHarvester - Goals

• Capturing informal learning/knowledge from SSIS

• Providing data for eLearning frameworks, e.g. Didaskon

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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

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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, ...

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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

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IKHarvester – API specification

URLHTTP

MethodDescription

http://server/ikh/soa/[type] GETReturns available LOs or LOs of the specified type (type parameter)

http://server/ikh/soa/$URI$/manifest GET Returns LOM for a specified LO

http://server/ikh/soa/$URI$/content GET Returns the content of a specified LO

http://server/ikh/soa/$URI$PUT / POST

Adds/updates LOM for a specified LO

http://server/ikh/soa/$URI$ DELETE Removes LOM for a specified LO

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Extensibility – support for new types of resources

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Comparison with existing tools

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Conclusions

• Features of Didaskon:– Dynamically builds courses for specific user– Uses formal courses described in LOM– Derives from IKHarvester which

• Captures knowledge from informal sources of information (wikis and blogs)

• Exposes harvested data in LOM