Developing Rich Interactive
eBooks to Teach Linked Open
Data to Professionals: Principles
and Processes
John Domingue, Knowledge Media Institute, The Open UniversitySTI International
St Petersburg, March 10, 2014
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Overview
• Motivation
• Design and delivery principles
• Channels
• Process
• Results
• Monitoring
• Future Work
• Summary
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Motivation (1/3)
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Motivation (2/3)
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http://www.mckinsey.com/mgi/publications/big_data/index.asp
Motivation (3/3)
Percentage increase in staff over next 2 years Approach taken to increasing staff
Big Data Analytics talent gap staff survey
PRINCIPLES
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‘Right’ Topic?
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Design Principles
Industrial relevance
Team curriculum design
External collaboration
Explicit learning goals
Use of realistic solutions
Use of real data
Use of real tools
Show scalable solutions
Eating our own dog food
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Delivery Principles
Multiple Channels
Open to format
Addressability
Integrated
High quality
Self-testing and reflection
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CHANNELS
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OU iTunes U Stats
• Open University on iTunes U was launched on
3rd June 2008
• Now 58 iTunes U Courses
• 65,138,000 downloads
• Over 9,015,700 visitors downloaded files
• Currently averaging 80,000-300,000 downloads
a week
• 449 collections containing 3,485 tracks (1,638
audio, 1,847 video) and 27 extras (PDF)
• 423 OpenLearn study units as eBooks (ePub),
representing over 5,000 hours of study
• Currently delivering an average of 0.3-1 TB of
data a week
ESWC Summer School
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PROCESS
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Expert Opinion
Gathering
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Data science curriculum session
Process Model
Collection of RawChapter Materials
Learning Objectives
SlidesFirst Version
Webinar –First Recording
Slides – Final Version
Webinar –Final Recording
Initial Version of eBook Chapter
Pre-final Version of eBook Chapter
Final eBook Chapterand online course
RESULTS
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Modules
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• Introduction and application scenarios
• Querying Linked Data
• Provisioning Linked Data
• Interaction with Linked Data
• Building Linked Data applications
• Scaling up
Module Formats
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iTunes U Course
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MONITORING
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Dataflow
Data Harvesting• Twitter
– keywords-based search & monitoring
• LinkedIn groups
– Linked Data Web, Semantic Web, ‘Ontologies, OWL-S,
SPARQL interest group’, Semantic Technologies Group,
Semantic Technologies, OWL/RDF
• W3C mailing lists
– SemanticWeb, LOD, Government Linked Data, Public-idp,
Public-ldp-wg, LifeScience, RDF Stream processing,
Semantic Open Data, Semantic Web Activity
• SlideShare - not monitored yet (RSS feed is
useless)
#30Social Media Monitoring Platform v.2 Dec 2013
Monitoring Numbers
• 185,000 tweets
• 773 linkedIn posts
• 898 messages from the W3 mail lists
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Monitoring Forums (1/2)
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Monitoring Forums (2/2)
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Monitoring Topics (1/2)
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Monitoring Topics (2/2)
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Monitoring People
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FUTURE WORK
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www.ict-forge.eu
@ICT_FORGE
FORGE is bringing the FIRE and eLearning worlds together
FIRE (Future Internet
Research and
Experimentation)
high-performance
test-beds
Forging Online Education through FIRE
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Summary
• Need to fill skilled data scientists gap
• Successful development and deployment of open educational resources– Multi-skilled course team
– Strong links to industrial requirements
– Rich multimedia interactive eBooks
– Explicit learning goals
– Real data and tools
– Multi channel delivery
• Linked data based monitoring
• Future work to deploy in new contexts – EU job skills dashboard
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The teamThanks to all contributors! Alexander Mikroyannidis, OU
Alice Carpentier, STI-R
Andreas Harth, KIT
Andreas Wagner, KIT
Andriy Nikolov, fluidOps
Barry Norton, Ontotext
Alex Simov, Ontotext
Marin Dimitrov, Ontotext
Daniel M. Herzig, KIT
Elena Simperl, STI-R/Univ. of Southampton
Günter Ladwig, KIT
Inga Shamkhalov, KIT
Jacek Kopecky, OU
John Domingue, OU
Juan Sequeda, Capsenta/University of Texas
Kalina Bontcheva, University of Sheffield
Lyndon Nixon, STI-R
Maria Maleshkova, KIT
Maria-Esther Vidal, Univers. Simon Bolivar
Maribel Acosta, KIT
Michael Meier, fluidOps
Ning Li, OU
Paul Mulholland, OU
Peter Haase, fluidOps
Richard Power, OU
Steffen Stadtmüller, KIT
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@euclid_project euclidproject euclidproject
http://www.euclid-project.eu
Other channels
eBook Course