Final CfP & deadline extension: 1st International Workshop on Learning Analytics and Linked Data (LALD2012)

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CALL FOR PAPERS
1st International Workshop on Learning Analytics and Linked Data (#LALD2012)
in conjunction with the 2nd Conference on Learning Analytics and 
Knowledge (LAK’12)
29.04. - 02.05.2012, Vancouver (Canada).

Jointly organized by the http://linkededucation.org initiative and the 
EATEL SIG dataTEL (http://bit.ly/datatel).

Workshop website: http://lald.linkededucation.org/
EXTENDED Submission deadline full and short papers: 28.03.2012
Submission deadline extended abstracts : 10.04.2012
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SCOPE
The main objective of the 1st International Workshop on Learning 
Analytics and Linked Data (#LALD2012) is to connect the research efforts 
on Linked Data and Learning Analytics to create visionary ideas [a] and 
foster synergies between both research fields. Therefore, the workshop 
will collect, explore, and present datasets, technologies and 
applications [b] for Technology-Enhanced Learning (TEL) to discuss 
Learning Analytics approaches which make use of educational Linked data.

BACKGROUND
In TEL, a multitude of datasets exists containing detailed observations 
of events in learning environments [c] that offer new opportunities for 
teaching and learning. The available datasets can be roughly 
distinguished between (a) Linked Data - Open Web Data and (b) Personal 
learning data from different learning environments. Open Web data covers 
educational data publicly available on the Web, such as Linked Open Data 
(LOD) published by institutions about their courses and other resources. 
It also includes the emergence of LD-based metadata schemas and 
TEL-related datasets. The main driver in the adoption of the LOD 
approach in the educational domain is the enrichment of the learning 
content and the learning experience by making use of various connected 
data sources.

Personal learning data from learning environments originate from 
tracking learners’ interactions with tools, resources or peers[d]. The 
main driver for analyzing these data is the vision of personalized 
learning that offers potential to create more effective learning 
experiences through new possibilities for predicting and reflecting the 
individual learning process.

To this end, Learning Analytics can be seen as an approach which brings 
together two different views: (i) the external view on publicly 
available Web data and (ii) an internal view on personal learner data, 
e.g. data about individual learning activities and histories. Learning 
Analytics aims at combining these two in a smart and innovative way to 
enable advanced educational services, such as recommendation (a) of 
suitable educational resources to individual learners, (b) peer students 
or external expert to cooperate with.

TOPICS
The workshop is looking for contributions touching the following topics.

Educational (Linked) Data
- Evaluating, promoting, creating and clustering of educational 
datasets, schemas and vocabularies
- Use of LOD for educational purposes
- Feasibility of standardization of educational datasets to enable 
exchange and interoperability
- Sharing of educational datasets among TEL researchers

Data Technologies:
- Innovative TEL applications that make large-scale use of the available 
open Web of data
- Real-world educational applications that exploit the Web of Data
- Tools to use and exploit educational Linked Open Data[e]
- Technologies for the exploration of educational datasets, i.e., for 
filtering, interlinking, exposing, adapting, converting and visualizing 
educational datasets
- Real-world applications that show a measurable impact of Learning 
Analytics

Evaluation of Technologies and Datasets:
- (Standardized) evaluation methods for Learning Analytics
- Descriptions of data competitions

Privacy and Ethics:
- Policies on ethical implications of using educational data for 
learning analytics (privacy and legal protection rights)
- Guidelines for the anonymisation and sharing of educational data for 
Learning Analytics research

SUBMISSION
The workshop is looking for different types of submissions. We accept 
regular full papers (8-14 pages), short papers (4-6 pages). Moreover, we 
are interested in datasets that can then be openly used in evaluating 
TEL recommender systems. Above all, we encourage you to demonstrate your 
data products and tools even if they are in a premature state. Datasets 
and demonstrations should be submitted together with an extended 
abstract submission (up to 2 pages). For all paper submissions we 
require formatting according to the Springer LNCS template 
http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

Submissions should be submitted through the conference management tool 
ginkgo: http://ginkgo.cs.upb.de/events/lald12

All submitted papers will be peer-reviewed by at least two members of 
the program committee for originality, significance, clarity, and 
quality. Final versions of accepted submissions will be published in the 
CEUR-WS.org workshop proceedings and most promising contributions will 
be invited to the 2nd Special Issue on dataTEL at the International 
Journal of Technology Enhanced Learning (IJTEL). In addition, the 
authors are asked to contribute short summaries of their submissions to 
the dataTEL group space at TELeurope to encourage early information 
sharing and discussion also with third persons. Based on workshop 
submissions, the organizers will identify most pressing research 
challenges to structure the workshop.

Please direct any questions to hendrik.drachsler[at]ou.nl

IMPORTANT DATES
28.03.2012 EXTENDED Submission deadline for full and short papers
10.04.2012 Submission deadline for extended abstracts
12.04.2012 Notification of acceptance
26.04.2012 Submission deadline for final papers
29.04.2012 Workshop
30.04. - 02.05.2012 LAK Conference

ORGANIZERS
Hendrik Drachsler; Open University of the Netherlands, NL
Stefan Dietze; L3S Research Center, DE
Mathieu d’Aquin; The Open University, UK
Wolfgang Greller; Open University of the Netherlands, NL
Jelena Jovanovic; University of Belgrade, SR
Abelardo Pardo; University Carlos III of Madrid, ES
Wolfgang Reinhardt; University of Paderborn, DE
Katrien Verbert; K.U.Leuven, BE

PROGRAMME COMMITTEE:
Hanan Ayad, Desire2Learn, Canada
Charalampos Bratsas, Aristotle University of Thessaloniki, Greece
Philippe Cudré-Mauroux, University of Fribourg, Switzerland
Nikolas Dovrolis, Democritus University of Thrace, Greece
Erik Duval, K.U. Leuven, Belgium
Martin Ebner, TU Graz, Austria
Dragan Gasevic, Athabasca University, Canada
Christian Glahn, ETH Zuerich, Switzerland
Ebner Hannes, Royal Institute of Technology (KTH), Sweden
Tom Heath, Talis, UK
Gawesh Jawaheer, City University London, United Kingdom
Eleni Kaldoudi, Democritus University of Thrace, Greece
Marco Kalz, Open University of the Netherlands, The Netherlands
Carsten Keßler, University of Münster, Germany
Nikos Manouselis, AgroKnow, Greece
Ivana Marenzi, L3S Reseach Center, University of Hannover, Germany
Felix Mödritscher, Vienna University of Economics and Business, Austria
Olga Santos, aDeNu Research Group, UNED, Spain
Melody Siadaty, Athabasca University, Canada
Peter Sloep, Open University of the Netherlands, The Netherlands
Markus Specht, Open University of the Netherlands, The Netherlands
Milan Stankovic, University Paris-Sorbonne, France
http://www.youtube.com/watch?v=Z13kJOIxq6E
Davide Taibi, Institute for Educational Technologies, Italian National 
Research Council, Italy
Dhavalkumar Thakker, University of Leeds UK
Fridolin Wild, Open University, United Kingdom
Martin Wolpers, FIT Fraunhofer, Germany
Hong Qing Yu, Open University, United Kingdom

Received on Monday, 19 March 2012 14:12:54 UTC