Recommender Systems for Learning

84 Pages·2013·1.051 MB·English

by  Nikos Manouselis, Hendrik Drachsler, Katrien Verbert, Erik Duval (auth.)

SpringerBriefs in Electrical and Computer
Engineering
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For furthervolumes:
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Nikos Manouselis Hendrik Drachsler
•
Katrien Verbert Erik Duval
•
Recommender Systems
for Learning
123
Nikos Manouselis Katrien Verbert
Agro-KnowTechnologies KULeuven
Athens Leuven
Greece Belgium
Hendrik Drachsler ErikDuval
OpenUniversity oftheNetherlands KULeuven
Heerlen Leuven
The Netherlands Belgium
ISSN 2191-8112 ISSN 2191-8120 (electronic)
ISBN 978-1-4614-4360-5 ISBN 978-1-4614-4361-2 (eBook)
DOI 10.1007/978-1-4614-4361-2
SpringerNewYorkHeidelbergDordrechtLondon
LibraryofCongressControlNumber:2012940235
(cid:2)TheAuthors2013
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Preface
Recommender systems are extremely popular as a research and application area,
with various interesting application domains such as e-commerce, entertainment,
and others. Nevertheless, it was only around early 2000 when the first notable
applications appeared in the domain of education, since relevant work was
generally considered to be connected to the area of adaptive educational
systems.
Today, research around recommender systems in an educational context has
significantly increased. Responding to a growing interest, this book expands the
relevantchapteronRecommenderSystemsinTechnologyEnhancedLearning(by
Manouselis, Drachsler, Vuorikari, Hummel and Koper) that was published in the
Springer Recommender Systems Handbook (2011) to provide an extensive and
in-depth analysis of the recommender systems currently found in the relevant
literature. The book briefly introduces recommender systems for learning and
discusses a wide and representative sample of issues that people working on
systems should be expecting to face. It serves as an overview of work in this
domainandthereforeespeciallyaddressespeoplewhoarestudyingorresearching
relevant topics and want to position their work in the overall landscape.
Thebibliographycoveredbythisbookisavailableinanopengroupcreatedat
the Mendeley research platform1 and will continue to be enriched with additional
references.Wewouldliketoencouragethereadertosignupforthisgroupandto
connecttothecommunityofpeopleworkingonthesetopics,gainingaccesstothe
1 http://www.mendeley.com/groups/1969281/recommender-systems-for-learning/
v
vi Preface
collectedblibliographybutalsocontributingpointerstonewrelevantpublications
within this very fast emerging domain.
Wehopethatyouwillenjoyreadingthisbookasmuchasweenjoyedworking
on it.
Nikos Manouselis
Hendrik Drachsler
Katrien Verbert
Erik Duval
Acknowledgments
Wewouldliketothankallthepeoplewhohaveinspiredthisworkandcontributed
to the discussion around these topics in various fora and opportunities. Particular
appreciationisreservedforourco-authorsofthechapteruponwhichthisworkhas
developed and expanded: Riina Vuorikari, Hans Hummel, and Rob Koper. We
also thank Laura Gavrilut for her supportin proof-reading the final version of the
book.
The work presented in this book has been carried out with European
Commissionfundingsupport.Morespecifically,theworkofNikosManouselishas
been supported by the EU projects CIP PSP VOA3R (http://www.voa3r.eu) and
FP7 agINFRA (http://www.aginfra.eu). The work of Hendrik Drachsler was
fundedbytheNeLLLfundingbodyinthecontextoftheAlterEgoproject.Katrien
Verbert is a Postdoctoral Fellow of the Research Foundation Flanders (FWO).
Part of this work was also supported by the SIG dataTEL of the European
Association of Technology Enhanced Learning and the former dataTEL Theme
Team of the STELLAR Network of Excellence (grant agreement no. 231913).
Thispublicationreflectstheviewsonlyoftheauthors,andthefundingbodiesand
agencies cannot be held responsible for any use that may be made of the
information contained therein.
vii
Contents
1 Introduction and Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Recommender Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Definitions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Relevant Systems in Educational Applications . . . . . . . . . . . . . 6
1.3.1 Adaptive Educational Hypermedia . . . . . . . . . . . . . . . . 6
1.3.2 Learning Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.3 Educational Data Mining and Learning Analytics. . . . . . 10
1.3.4 Similarities and Differences. . . . . . . . . . . . . . . . . . . . . 11
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 TEL as a Recommendation Context . . . . . . . . . . . . . . . . . . . . . . . 21
2.1 TEL Recommendation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.1.1 Defining the TEL Recommendation Problem. . . . . . . . . 21
2.1.2 Identifying the TEL Recommendation Goals . . . . . . . . . 24
2.1.3 Identifying the TEL Context Variables . . . . . . . . . . . . . 28
2.2 Data Sets to Support TEL Recommendation. . . . . . . . . . . . . . . 29
2.2.1 Collecting TEL Data Sets . . . . . . . . . . . . . . . . . . . . . . 29
2.2.2 Collected Data Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.2.3 Usefulness for TEL Recommender Systems. . . . . . . . . . 33
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3 Survey and Analysis of TEL Recommender Systems. . . . . . . . . . . 37
3.1 Framework for Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.2 Survey Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.2.1 General Overview of Sample . . . . . . . . . . . . . . . . . . . . 42
3.2.2 Analysis According to Framework . . . . . . . . . . . . . . . . 52
3.2.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
ix
x Contents
4 Challenges and Outlook. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.1 Challenges for TEL Recommendation . . . . . . . . . . . . . . . . . . . 63
4.1.1 Pedagogy and Cognition . . . . . . . . . . . . . . . . . . . . . . . 64
4.1.2 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.1.3 Data Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.1.4 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.1.5 Visualisation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.1.6 Virtualisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Acronyms
AEH Adaptive Educational Hypermedia
CAM Contextualised Attention Metadata
CSCL Computer Supported Collaborative Learning
EDM Educational Data Mining
IR Information Retrieval
ITS Intelligent Tutoring System
KSA Knowledge, Skills, Abilities
LAK Learning and Knowledge Analytics
LMS Learning Management System
LOM Learning Object Metadata
MCDM Multi-Criteria Decision Making
MUPPLE Mash-Up Personal Learning Environment
OAI Open Archives Initiative
PSLC Pittsburgh Science of Learning Center
TEL Technology Enhanced Learning
VLE Virtual Learning Environments
xi
Chapter 1
Introduction and Background
Abstract Inthischapter,westartwithashortintroductiontotheincreasethathas
beenwitnessedinthepastfewyearsinapplicationsofrecommendersystemsatthe
TELdomain.Thenweprovidesomebackgroundontheareaofrecommendersys-
tems,bydefiningrecommendersystemsandoutliningtheirbasictypes.Acompari-
sonwithrelevantworkinTEListried,particularlyfocusingonadaptiveeducational
hypermedia, learning networks, educational data mining, and learning analytics.
Adiscussionontheirsimilaritiesanddifferencesisalsomade,sothatrelevantwork
canbebetterpositionedintheTELresearchlandscape.
1.1 Introduction
Technologyenhancedlearning(TEL)aimstodesign,developandtestsociotechnical
innovationsthatwillsupportandenhancelearningpracticesofbothindividualsand
organisations.Itisthereforeanapplicationdomainthatgenerallycoverstechnologies
thatsupportallformsofteachingandlearningactivities.Sinceinformationretrieval
(intermsofsearchingforrelevantlearningresourcestosupportteachersorlearners)
isapivotalactivityinTEL,thedeploymentofrecommendersystemshasattracted
increasedinterest.
This should be more or less expected since a traditional problem in TEL has
beenthebetterfindabilityof(mainly)digitallearningresources.Forinstance,digital
learning content is being regularly produced, organised and published in different
typesofTELenvironmentssuchas(Ochoa2011):
1. LearningObjectRepositorieslikeLearningResourceExchange,1Connexions2
orMaricopaExchange3;
1http://lreforschools.eun.org
2http://cnx.org
3http://www.mcli.dist.maricopa.edu/mlx/
N.Manouselisetal.,RecommenderSystemsforLearning, 1
SpringerBriefsinElectricalandComputerEngineering,
DOI:10.1007/978-1-4614-4361-2_1,©TheAuthors2013