Using Linked Data in Learning AnalyticsLAK 2013 tutorial

Mathieu d’Aquin (@mdaquin, mdaquin.net)(Knowledge Media Institute, The Open University, UK)Stefan Dietze (L3S Research Center, DE)Hendrik Drachsler(CELSTEC, Open Universiteit Nederland, NL)Eelco Herder(L3S Research Center, DE)

Why a Linked Data Tutorial at LAK 2013?A Naïve view

Learning Analytics is an application of data analytics on educational data, in learning environment and for the purpose to improve the learning and teaching experience.

Linked Data is a set of technologies and principles to expose, publish and interconnect data on the Web. It is very popular nowadays for opendata, eGovernment, academia and the industry because of the flexibility and the global integration possibilities it provides.

So, Linked Data used to find, collect and process large amounts of interconnected data to be used in analytics. But It is not only the input! Can be used to complete local data, enrichment them, or for interpretation of the results.

Schedule8.30 Intro to the tutorial Linked data and its potential in learning analytics scenarios Basics of manipulating linked data

10.30 Coffee break

11.00 Using Linked Data in Analytics Tools Evaluation of the Linked Data applications

12.30 Lunch

13.30 Introduction to the LAK Data challenge Presentations from the LAK Data Challenge particiants

15.30 Tea break

16.30 Current state of Linked Data in Learning Analytics Results of the challenge Wrap up

17.30 Finished

The LAK Data Challenge (preview)

Soude Fazeli – Open Universiteit Nederland (Netherlands). Socio-semantic Networks of Research Publications in the Learning Analytics Community

Michael Derntl, Nikou Günnemann and Ralf Klamma – RWTH Aachen (Germany). A Dynamic Topic Model of Learning Analytics Research

Ricardo Alonso Maturana, María Elena Alvarado, Susana Lopez-Sola, María José Ibáñez and Lorena Ruiz Elósegui – GNOSS (Spain). Linked Data based applications for Learning Analytics Research: faceted searches, enriched contexts, graph browsing and geographic visualisation

Nikola Milikic, Uros Krcadinac, Jelena Jovanovic, Bojan Brankov and Srdjan Keca – University of Belgrade, UZROK Labs (Serbia).Paperista: Visual Exploration of Semantically Annotated Research Papers

Sadia Nawaz, Farshid Marbouti and Johannes Strobel – Purdue University (United States). Analysis of the Community of Learning Analytics

Bernardo Pereira Nunes and Besnik Fetahu – L3S Research Center (Germany). Cite4Me: Semantic Retrieval and Analysis of Scientific Publications

Davide Taibi, Ágnes Sándor, Duygu Simsek, Simon Buckingham Shum, Anna De Liddo and Rebecca Ferguson – Italian National Research Council, Xerox Research Center (France), The Open University (UK). Visualizing the LAK/EDM Literature Using Combined Concept and Rhetorical Sentence Extraction

Amal Zouaq, Srecko Joksimovic and Dragan Gasevic – Royal Military College of Canada, Simon Fraser University, Athabasca University (Canada). Ontology Learning to Analyze Research Trends in Learning Analytics Publications

Your guides to the wild world of linked data

Mathieu@mdaquin

Stefan@stefandietze

Hendrik@hdrachsler

Eelco@eelcoherder

(put he is not actually here)

Linked data and its potential in learning analytics scenarios

Linked Data

Open University Website

Open UniversityVLE

KMi Website

Mathieu’s Homepage

Mathieu’s List of

PublicationsMathieu’s

Twitter

The Web

M366 Coursepage

Person: Mathieu

Publication: Pub1

Organisation:The Open University

Course: M366

Country: Belgium

Book: Mechatronics

author

workFor

availableIn

offers

setBook

The Web of Linked Data

From Linked Data to the Semantic Web

Gene Ontology

FMA OntologyLODE

BIBO

Geo Ontology

DBPedia Ontology

Dublin Core

FOAF

DOAP

SIOC

Music Ontology

Media Ontology

rNews

Example: data.open.ac.uk

Data.open.ac.uk

Course information: 580 modules/ description of the course, information about the levels and number of

credits associated with it, topics, and conditions of enrolment.

Research publications: 16,000 academic articles / information about authors, dates, abstract and venue of the

publication.

Podcasts: 2220 video podcasts and 1500 audio podcats / short description, topics, link to a

representative image and to a transscript if available, information about the course the podcast might relate to and license information regarding the content of the podcast.

Open Educational Resources: 640 OpenLearn Units / short description, topics, tags used to annotate the resource, its

language, the course it might relate to, and the license that applies to the content.

Youtube videos: 900 videos / short description of the video, tags that were used to annotate the video,

collection it might be part of and link to the related course if relevant.

University buildings: 100 buildings / address, a picture of the building and the sub-divisions of the building

into floors and spaces.

Library catalogue: 12,000 books/ topics, authors, publisher and ISBN, as well as the course related.

Others…

A global data space for education data

The Open University

University of Bristol

University of Southampton

mEducator

University of Muenster, DEOrganicEduNet

Data.gov.uk education

Orgs., Buidings, Locations

Learning resources

Research ouputs

8. April 2023LinkedUp – Mathieu d‘Aquin 12

http://data.linkededucation.org/linkedup/catalog

What’s the use in Learning Analytics

From Bienkowski, Feng, and Means areas of LA/EDM applications

• Modeling user knowledge, behavior, and experience… and connect them to information about the context of learning

• Creating profiles of users… that can be interlinked through common objects

• Modeling knowledge domains… through online knowledge sources that can be numerous and collectively built

• Trend analysis… that can be interpreted through related them to external sources of information

• Personalization and adaptation… using indirect connections to other reference entities

Original image from George Siemenshttp://www.elearnspace.org/blog/2010/08/25/what-are-learning-analytics/

Data

Integration

Understanding

Example of simple application: Map of OU buildings

Interactive map of Open University Buildings in the UK

Each dot is a location where buildings can be found. Going over the dot give information about the building there (floors, spaces, car-parks, etc.)

Spaces

Floors

ID Address Post-code

Buildings

bat1

bat1-address

Postcode-mk76aa

name “Berrill building”

data.open.ac.ukMilton Keynes

inDistrict

Buckinghamshire

inCounty

Mk76aa-location

location

lat long

52.024924 -0.709726

data.ordnancesurvey.co.uk

Simple recommendation: Study at the OU

Each topic as a linked data URI. Each course as a linked data URI. Each resource as a linked data URI. They are all connected. Use SPARQL to answer the question:

“What are the resources related to this topic or to courses on this topic”

Less simple recommendation: Talis Aspire

Lecturers from different universities put their reading lists online. Publishing using the principles of linked data means that all resources are globally identified, creating a network of resources and reading lists.Recommendations can then trivially exploit globally all these local contributions.

Even less simple recommendation:DiscOU

data.open.ac.uk

Semantic Indexing

Semantic Index

Named Entity

RecognitionPodcasts, OpenLearn Units and Articles

Semantic Entities (Dbpedia)

Indexes

BBC Programme or iPlayer page

Synopsis

Similarity-Based Search

Indexes

Interface

Resource descriptions

Resources URIs + common topics

Not one scenario: An infinite recombination of data and purposes

Complex analytics in a lightweight way

http://uciad.info

Analytics across datasets

Academics in “Arts and Humanities” most often involved with the media (in number of news items)

Topics most commonly mentioned by news outlets own by the BBC (in number of news items)

From the Open University From news clippings From DBpedia.org

Complex analytics with rich background information

Basics of manipulating linked data

Agenda

URIs – the basis

RDF – the representation language

Ontologies/Vocabularies – for schemas and models

SPARQL – for querying

SPARQL Update – for modifying (but we won’t say much about this)

URIs – three roles

Example:

http://data.aalto.fi/id/courses/noppa/dept_T3030

An anchor for linkingLet’s say you have worked

there. You – worked-at this URI

An identifier for a data entity

Here, the Department of Media Technology of the University of

Aalto, Finland

An access point to representation(s) of

the data entityIn possibly different formats…

URI resolving

http://data.aalto.fi/id/courses/noppa/dept_T3030

8. April 2023 29

In the browser(Accept: text/html)

curl -H "Accept: application/rdf+xml" -L http://data.aalto.fi/id/courses/noppa/dept_T3030

<rdf:Description rdf:about="http://data.aalto.fi/data/id/courses/noppa/dept_T3030"> <rdfs:label>RDF description of Department of Media Technology</rdfs:label> <foaf:primaryTopic> <aiiso:Department rdf:about="http://data.aalto.fi/id/courses/noppa/dept_T3030"> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5077"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2211"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.3101"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5006"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5600"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4950"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.1100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6596"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1220"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4360"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5701"/> <aiiso:code>T3030</aiiso:code> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4210"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5070"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4400"/> <foaf:name xml:lang="en">Department of Media Technology</foaf:name> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5310"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5020"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.1110"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6595"/> <foaf:name xml:lang="sv">Institutionen för mediateknik</foaf:name> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1202"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5700"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5600"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.1124"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4900"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.2300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5360"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.4101"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5200"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2400"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5030"/> <aiiso:part_of> <rdf:Description rdf:about="http://data.aalto.fi/id/courses/noppa/org_SCI"> <aiiso:organization rdf:resource="http://data.aalto.fi/id/courses/noppa/dept_T3030"/> </rdf:Description> </aiiso:part_of> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5700"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4800"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5502"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5350"/>

RDF – graph data model for linked data and the Web

Basic idea: URIs and literals (String, integers) are nodes - connected by links labelled by properties (themselves identified as URIs)

http://data.aalto.fi/id/courses/noppa/dept_T3030

“Department of Media Technology”

foaf:name

aiiso:Departmentrdf:type

http://data.aalto.fi/id/courses/noppa/org_SCI

aiiso:part_ofaiiso:School

rdf:type

“School of Science”foaf:name

http://data.aalto.fi/id/courses/noppa/course_Inf-0.1202

aiiso:teaches

aiiso:Courserdf:typeteach:courseTitle

“Filosofia” 

“fi”dc:language

RDF+XML

<aiiso:Department rdf:about="http://data.aalto.fi/id/courses/noppa/dept_T3030"> <aiiso:code>T3030</aiiso:code> <foaf:name xml:lang="en">Department of Media Technology</foaf:name> <foaf:name xml:lang="sv">Institutionen för mediateknik</foaf:name> <aiiso:part_of rdf:resource="http://data.aalto.fi/id/courses/noppa/org_SCI"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5077"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2211"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.1100"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.6596"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5300"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.1220"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4360"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5701"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4210"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5070"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.4400"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_Inf-0.4101"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-75.5200"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.2400"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5030"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.5700"/> <aiiso:teaches rdf:resource="http://data.aalto.fi/id/courses/noppa/course_T-111.4800"/>

Other syntaxes…

… Ntriple, Turtle and JSON-LD

Simpler to a certain extent, but same principles

Spaces

Floors

ID Address Post-code

Buildings

bat1

bat1-address

Postcode-mk76aa

name “Berrill building”

data.open.ac.ukMilton Keynes

inDistrict

Buckinghamshire

inCounty

Mk76aa-location

location

lat long

52.024924 -0.709726

data.ordnancesurvey.co.uk

Remember…

Ontologies and Vocabularies

Role: Provide common definitions for the types (classes) and properties (relations) used in the RDF representations, and their expected behaviour (meaning)

Vocabularies and ontologies we have already seen:• AIISO: Academic Institution Internal Structure Ontology• DC: Dublin Core• FOAF: Friend of a Friend (for people and their connections)• TEACH: For courses and academic programmes

Use the same mechanisms as Linked Data: • Classes and properties have URIs• They connect through special properties (rdf:type, rdfs:domain, rdfs:range,

rdfs:subClassOf, etc.)

Formal ontologies: define more precisely the intended meaning of types and properties based on logical constructs

8. April 2023LinkedUp – Author Name 35

Example: AIISO

foaf:Organization

aiiso:School

rdfs:subClassOf

aiiso:College

aiiso:Course

aiiso:Department

rdfs:subClassOf

rdfs:subClassOf

aiiso:Institution

rdfs:subClassOf

aiiso:Faculty

rdfs:subClassOf

aiiso:KnowledgeGrouping

rdfs:subClassOf

aiiso:Module

rdfs:subClassOf

aiiso:Programme

rdfs:subClassOf

aiiso:part_of

foaf:Agent

aiiso:responsibleFor

aiiso:responsibleFor

aiiso:teaches

Example: BIBO

bibo:Article

bibo:AcademicArticle

bibo:Document

bibo:Book

bibo:AudioVisualDocument

bibo:DocumentPart

bibo:BookSection

bibo:Chapter

bibo:EditedBook

bibo:Issue

bibo:Journal

rdfs:subClassOf

rdfs:subClassOfrdfs:subClassOf

rdfs:subClassOf

rdfs:subClassOf

rdfs:subClassOfrdfs:subClassOf

bibo:partOf

All bibo:partOf

<=1 bibo:partOf

<=1 bibo:partOf

Querying: SPARQL

ASK query: is this true?ask {<http://data.aalto.fi/id/courses/noppa/dept_T3030> a

aiiso:Department} (is it a department?)ask{<http://data.aalto.fi/id/courses/noppa/dept_T3030> dc:subject ?x}

(is there a subject to this department?)

Select query: Get me some dataselect ?org ?name where { ?x a aiiso:Department. ?x aisso:part_of ?org. ?org foaf:name ?name. filter( ?x != <http://data.aalto.fi/id/courses/noppa/dept_T3030> ) } order by ?name limit 100(get the organisations with names that have department, except T3030)

Construct query: Build a (sub) RDF graphconstruct {?agent1 foaf:knows ?agent2} where {?agent1 aiiso:responsibleFor ?x. ?agent2:responsibleFor ?x}(Construct of graph of people knowing each-other because of being responsible from the same thing)

Querying: SPARQL

SPARQL is also a protocol for Web-based data endpoints…

Example

select distinct ?course where {?course <http://data.open.ac.uk/saou/ontology#isAvailableIn> <http://sws.geonames.org/2328926/>.?course a <http://purl.org/vocab/aiiso/schema#Module>

}

Open University courses available in Nigeria (http://sws.geonames.org/2328926/) on http://data.open.ac.uk/query

Example

select distinct ?q (count(distinct ?t) as ?n) where { ?q a <http://purl.org/net/mlo/qualification>. ?q <http://data.open.ac.uk/saou/ontology#hasPathway> ?p. ?p <http://data.open.ac.uk/saou/ontology#hasStage> ?s. {{?s <http://data.open.ac.uk/saou/ontology#includesCompulsoryCourse> ?c} union {?s <http://data.open.ac.uk/saou/ontology#includesOptionalCourse> ?c}}. ?c <http://purl.org/dc/terms/subject> ?t. [] <http://www.w3.org/2004/02/skos/core#hasTopConcept> ?t.} group by ?q order by desc(?n)

How many top level subjects are represented in individual Open University qualifications on http://data.open.ac.uk/query

Example

select ?broader ?term ?narrowerwhere { graph npgg:subjects { ?subject skos:prefLabel ?term . ?subject skos:broader [ skos:prefLabel ?broader ] . ?_ skos:broader ?subject ; skos:prefLabel ?narrower . } filter(regex(?term, "^Stem cells$", "i"))}order by ?broader ?narrower

Broader and narrower terms for "Stem cells“ on http://data.nature.com/query

Example

select ?doi ?datawhere { ?doi a npg:Article ; npg:hasDataCitation [ npg:hasLink [ ?_ ?data ] ; npg:type ?type ] . filter(regex(?type, "pdb"))}limit 25

Data citations to the Protein Database on http://data.nature.com/query

SPARQL update

Delete querydelete {?x ?p ?y} where { ?x a aiiso:Course. ?x ?p ?y. ?a1 aiiso:responsibleFor ?x. ?a2 aiiso:responsibleFor ?x. filter ( ?a1 != ?a2 )}

Insert query insert {?x a onto:WeirdCourse} where{ ?x a aiiso:Course. ?a1 aiiso:responsibleFor ?x. ?a2 aiiso:responsibleFor ?x. filter ( ?a1 != ?a2 )}

Basic take away message

Linked data is about using the web architecture for sharing and connecting data, with some form of meaningful interpretation (semantic web)

Potential in Learning Analytics processes: as input data, for data integration, for enrichment, for interpretation

Based on simple technologies for web-based access to the data: URI, RDF, Web Schemas, SPARQL

Links and References

http://linkeddata.org - http://linkeddatabook.com http://data.open.ac.uk - http://lucero-project.info http://linkeduniversities.org - http://linkededucation.orghttp://linkedup-project.eu - http://linkedup-challenge.orghttp://data.linkededucation.org/linkedup/catalog/ http://talisaspire.com/ - http://discou.info - http://uciad.info http://http://www.w3.org/TR/rdf-sparql-query/ http://www.w3.org/TR/sparql11-update/

d'Aquin, M. and Jay, N. Interpreting Data Mining Results with Linked Data for Learning Analytics: Motivation, Case Study and Direction, LAK 2013, http://oro.open.ac.uk/36660/

Kessler C., d’Aquin M. and Dietze S. (eds) Semantic Web Journal Special Issue on Linked Data for Science and Education. http://iospress.metapress.com/content/m87017012802/

d’Aquin M. Linked Data for Open and Distance Learning. Commonwealth of Learnin report. http://www.col.org/resources/publications/Pages/detail.aspx?PID=420

d'Aquin, M., Allocca, C. and Collins, T. DiscOU: A Flexible Discovery Engine for Open Educational Resources Using Semantic Indexing and Relationship Summaries, Demo ISWC 2012. http://data.open.ac.uk/applications/iswc2012-demo.pdf

Bienkowski, M., Feng M. and Means, B. Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. U.S. Department of Education, Office of Educational Technology http://www.ed.gov/edblogs/technology/files/2012/03/edm-la-brief.pdf