Wilbert Kraan

Idealism and Pragmatism in Linked Educational Data

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?

Problems and solutions

Photo credit: Justn Baeder

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

The semantic web vision

• The problem the semantic web addressed:

– Decision support and automation

• Semantic web solution:– Sharing knowledge

representations– Inferencing– Semantic web

agents

Current decision support practice

The semantic web vision and learning• Adaptive educational

hypermedia• The problem:

– Scaling up personalised learning

• The solution:– Sharing knowledge

representations– Reasoning– Large datasets

Current adaptive educational hypermedia practice

Semantic web solutions

• The problem the semantic web addressed:

– Decision support and automation

• Semantic web solution:– Sharing knowledge

representations– Inferencing– Semantic web

agents

Semantic web solutions currently in wide use

Schema.org

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

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

What's a topic map?

"TopicMaps2Go". Via Wikipedia

What can a Topic Map do for learning?

Newcomb & Biezunski(2002) “A Draf Reference Model for ISO 13250 Topic Maps”

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

Semantic web solutions

• The problem the semantic web addressed:

– Decision support and automation

• Semantic web solution:– Sharing knowledge

representations– Inferencing– Semantic web

agents

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

Current widely used data sharing practice

The linked data vision and learning

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

Resource discovery; the Learner Journey navigator case

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?

Data integration; University of Bolton analytics project

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

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?

Questions, comments?

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 /