Informal Knowledge In E Learning

Slide 1: 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  Copyright 2007 Digital Enterprise Research www.deri.ie Institute. All rights reserved.

Slide 2: Presentation scope • Motivation • Formal and Informal learning • Didaskon project • IKHarvester project • Social Semantic Information Sources (SSIS) • Conclusions 2

Slide 3: 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 3

Slide 4: 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 4

Slide 5: 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 5

Slide 6: Didaskon Context 6

Slide 7: 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 7

Slide 8: IKHarvester - Informal Knowledge Harvester 8

Slide 9: 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 9

Slide 10: IKHarvester - Goals • Capturing informal learning/knowledge from SSIS • Providing data for eLearning frameworks, e.g. Didaskon 10

Slide 11: 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 11

Slide 12: 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, ... 12

Slide 13: 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 13

Slide 14: IKHarvester – API specification HTTP URL Description Method Returns available LOs or LOs of the http://server/ikh/soa/[type] GET 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 PUT / http://server/ikh/soa/$URI$ Adds/updates LOM for a specified LO POST http://server/ikh/soa/$URI$ DELETE Removes LOM for a specified LO 14

Slide 15: Extensibility – support for new types of resources 15

Slide 16: Comparison with existing tools 16

Slide 17: 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 17