idealism and pragmatism in linked educational data
DESCRIPTION
An exploration of idealist goals and pragmatic solutions for education that came out of the semantic web and linked data initiatives.TRANSCRIPT
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Wilbert Kraan
Idealism and Pragmatism in Linked Educational Data
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Overview
• Problems and solutions– Idealism and pragmatism
• The semantic web and learning– The vision and how far we realised it– Some of the solutions and how they fared
• The Linked Data and education– The vision and how far we realised it– Some of the solutions and how they fared
• What have we learned?
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Problems and solutions
• Is L in ke d E d u c a t io n a l D a ta :– a n e w s o lu t io n to a n e w p ro b le m– a b e t te r s o lu t io n to a p ro b le m w e a lre a d y k n o w
Idealistc vision
Pragmatc soluton
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The semantic web vision
• The problem the semantic web addressed:
– Decision support and automation
• Semantic web solution:– Sharing knowledge
representations– Inferencing– Semantic web
agents
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Current decision support practice
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The semantic web vision and learning• Adaptive educational
hypermedia• The problem:
– Scaling up personalised learning
• The solution:– Sharing knowledge
representations– Reasoning– Large datasets
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Current adaptive educational hypermedia practice
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Semantic web solutions
• The problem the semantic web addressed:
– Decision support and automation
• Semantic web solution:– Sharing knowledge
representations– Inferencing– Semantic web
agents
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Semantic web solutions currently in wide use
Schema.org
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Semantic web solutions and learning• The problem:
– Few opportunities to build knowledge explicitly and collaboratively (boosting retention)
• The semantic web tech solution:– Knowledge representations that are
–Explicit–Editable –Sharable
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Collaborative knowledge building: the BrainBank case• P a r t co n c e p t m a p p e r , p a r t e -p o r t fo l io• A llo w s le a r n e r s to b u ild th e ir o w n k n o w le d g e s to re• B u ilt o n IS O To p ic M a p s
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What's a topic map?
"TopicMaps2Go". Via Wikipedia
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What can a Topic Map do for learning?
Newcomb & Biezunski(2002) “A Draf Reference Model for ISO 13250 Topic Maps”
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Current collaborative knowledge building solutions
ab
123
Mind Map Guidelines
Style
Keywords
CenterClarity
Use
printcase UPPER and lower
lines organisedfor each
styleconnect
wordimagealone
center
radiate out
organicfree flowing
length same asword
image
outerthinnerless important
central thickermore important
develop
personal
outlines
orderhierarchy
Start image
c o l o r s
of topic
at least 3
Colors
Emphasis
images
codes
dimension
Links
"MindMapGuidlines"by Nicoguaro - Own work. Licensed under Creatve Commons Atributon-Share Alike 3.0 via Wikimedia Commons
WikiNizer
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Semantic web solutions
• The problem the semantic web addressed:
– Decision support and automation
• Semantic web solution:– Sharing knowledge
representations– Inferencing– Semantic web
agents
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The Linked Data vision
• The problem addressed:– Sharing data on the web
• Linked data solution:– recommended practices for exposing,
sharing, and connecting pieces of–Data– Information–Knowledge
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Current widely used data sharing practice
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The linked data vision and learning
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Problems solved with linked education data solutions • T y p e s o f a p p lic a t io n in th e L in k e d U p C h a lle n g e
Resource discoveryData integration
Knowledge representationOther
0
5
10
15
20
25
30
35
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Resource discovery; the Learner Journey navigator case
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Learner Journey Navigator
• Data sources:– Qualifcation and curriculum
authorities–Qualifcation identifers–Curriculum structures
– Schools, colleges and universities–Courses ofered–Achievements verifed
– Learners–Achievements– Interests
– Publishers–Learning resources
• Questions:– Where is the learner
in the curriculum?– What course can we
suggest they should pick next?
– What resources can help them get there?
– Who can help or inspire them?
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Data integration; University of Bolton analytics project
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What we have learned- pragmatic solutions• Linked data / semantic web tools are strong in:
– Near real time integration of limited numbers of heterogenous data sets–Typical use case is resource discovery, but
learning analytics could be the killer app• Because:
– Non-deterministic, but structured nature of RDF
– Flexible tool choice through interoperability standards
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What we have learned- idealistic vision• Aim high
– You might get there• The semantic web vision may yet happen, but:
– Not all aspects at once, or unifed– Only if enlightened self-interest is harnessed
• Adaptive educational hypermedia and collaborative knowledge construction are hard
– Is that because of technological limitations or human nature?
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Questions, comments?
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Licence
T h is p re s e n ta t io n “ Id e a l ism a n d P ra gm a t ism in L in ke d E d u c a t io n a l D a ta ”
b y W ilb e r t K ra a n , w .g .k ra a n @ o v o d .n e t
o f C e t is h t tp :/ / w w w .c e t is .a c .u k is l ic e n s e d u n d e r th e
C re a t iv e C o m m o n s A t t r ib u t io n 3 .0 U n p o r te d L ic e n c e
h t t p :/ / c re a t iv e c o m m o n s .o r g / lic e n s e s / b y / 3 .0 /