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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).
Submission deadline full and short papers: 14.03.2012
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 young
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 data or Linked Data
sources. During the workshop, an overview of available educational
datasets and related initiatives will be given. The participants
will have the opportunity to present their own research with
respect to educational datasets, technologies and applications and
discuss major challenges to collect, reuse and share these
datasets.
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; examples include (but are
not limited to), The Open University (UK), the National Research
Council (CNR, Italy), Southampton University (UK) or the mEducator
Linked Educational 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:
- 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
- Real-world educational applications that exploit the Web of
Data
- Tools to use and exploit educational Linked Open Data[e]
- Innovative TEL applications that make large-scale use of the
available open Web of data
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 paper (8-14 pages), short paper (4-6 pages).
Moreover, we are interested in anonymized 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 submissions
(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
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.
All questions and submissions should be sent to:
hendrik.drachsler[at]
ou.nl
IMPORTANT DATES
14.03.2012
Submission deadline for full and short papers
10.04.2012
Submission deadline for extended abstracts (describing data
sets and demonstrations)
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 (to be confirmed):
Markus Specht, Open University of the Netherlands, The
Netherlands
Peter Sloep, Open University of the Netherlands, The
Netherlands
Marco Kalz, Open University of the Netherlands, The Netherlands
Christian Glahn, ETH Zuerich, Switzerland
Erik Duval, K.U. Leuven, Belgium
Martin Wolpers, FIT Fraunhofer, Germany
Nikos Manouselis, AgroKnow, Greece
Olga Santos, aDeNu Research Group, UNED, Spain
Dragan Gasevic, Athabasca University, Canada
Felix Mödritscher, Vienna University of Economics and Business,
Austria
Fridolin Wild, Open University, United Kingdom
Gawesh Jawaheer, City University London, United Kingdom
Ebner Hannes, Royal Institute of Technology (KTH), Sweden
Hanan Ayad, Desire2Learn, Canada
Melody Siadaty, Athabasca University, Canada
Philippe Cudré-Mauroux, University of Fribourg, Switzerland
Carsten Keßler, University of Münster, Germany
Davide Taibi, Institute for Educational Technologies, Italian
National Research Council, Italy
Tom Heath, Talis, UK