Advanced Technologies for Supporting Open Access to Formal and Informal Learning

fantasicgilamonsterΔιαχείριση Δεδομένων

20 Νοε 2013 (πριν από 3 χρόνια και 4 μήνες)

77 εμφανίσεις


1

CALL FOR PAPERS


The 1
4
th

IEEE International Conference on Advanced Learni
ng
Technologies
-

ICALT20
1
4


Advanced
Technologies for
Supporting
Open Access to Formal and
Informal Learning




Athens, Greece,
July
7
-
10
, 201
4


http://www.ask4research.info/icalt/2014/



Track

12
:
Recommender Systems for Learning


(ReSyL@ICALT2014)

http://www.ask4research.info/icalt/2014/node/34


Track Program Chair
s


Hendrik Drachsler, Open University of Netherlands, Netherlands


Yanyan Li, B
eijing Normal University, China

(
liyy@bnu.edu.cn
)

[Co
-
ordinator]

Olga

C.
Santos,
Spanish National Universit
y for Distance Education, Spain



Track Description and Topics of Interest:


With the increasingly growth of multimedia resources in the various e
-
learning systems and online
learning communities, how to find and access useful information for learning and teaching has
become a big challe
nge. Recommendation methods, techniques and systems open an interesting
new approach to facilitate and support learning and teaching. The focus is to develop, deploy and
evaluate recommender systems that provide learners and teachers with meaningful guidan
ce in
order to help identify suitable learning resources, both in terms of digital learning content and
people resources (e.g. learners, experts, tutors), from a potentially overwhelming variety of
choices. This track aims to bring together researchers and

practitioners around the topics of
designing, developing and evaluating recommender systems in educational settings as well as
present the current status of research in this area. We welcome papers describing work in
progress and encourage submissions tha
t make datasets available to the community.


Topics of interest include but are not limited to:



User modeling for learning recommender systems



Affective computing in educational recommender systems



Multimedia information retrieval and recommendation for le
arning



Semantic Web technologies for recommendation



Data Mining and Web Mining for recommendation



Machine Learning for recommendation


2



Context modeling techniques for recommender systems



Recommendation algorithms and systems for learning



Data sets for learn
ing recommender systems



Explanation and visualization of recommendations



Evaluation criteria and methods for learning recommender systems



Members of
Track Program Committee


Peter Brusilovsky, Univer
sity of Pittsburgh,
USA

Ryan Baker, University Columbia
,
USA

Chi
-
Cheng Chang, National Taiwan Normal University, Taiwan

Su Cai, B
eijing Normal University, China


Soude Fazeli, Open Universiteit Nederland
, Netherlands


Peter Slo
ep, Open Universiteit Nederland
, Netherlands


Nikos Manouselis, Agro
-
Know Technologi
es & ARIADNE Foundation, Greece


Katrien Verbert, Katholieke Universiteit Leuven, Belgium


Rory Sie, E
cole
P
olytechnique
F
ederal de
L
ausanne
,
Switzerland


Davinia Hernández
-
Leo, U
niversitat Pompeu Fabra, Spain

Maren Sc
heffel, FIT Fraunhofer, Germany


Feli
x Mödritscher,University of Vienna, Austria


Stefan Dietze, L3S, Germany

Mojisola
Erdt
, TU Darmstadt, Germany


Kris J
ack, Mendeley, U
nited
K
ingdom


Xavier Ochoa, Escuela Su
perior Politecnica del Litoral,
Ecua
dor

Christoph Rensing
, TU Darmstadt, Germany


Mi
guel
-
Angel Sicilia
, University of Alcala, Spain


Julien Broisin,

IRIT Universite Paul Sabatier, France


Katrin Borcea
-
Pfitzmann, Dresden University of Technology, Germany

Jesus G. Boticario
, Spanish National University for Distance Education,

Spain



Rita
Kuo, Academic

and Industrial Research Centre &

Knowledge Square, Inc., Taiwan


Tiffany Tang, Kean University,
USA

Sergey Sosnovsky, CeLTech

&

DFKI, Germany


Beatriz Eugenia Florián Gaviria,

Universidad del Valle, Colombia

Amine
Chatti, RWTH Aachen, German
y



R
alf Klmma, RWTH Aachen, Germany