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https://ceur-ws.org/Vol-2354/w1preface.pdf
C&C@ITS2018
International Workshop on
Context and Culture In
Intelligent Tutoring Systems
Valéry Psyché 1, Isabelle Savard 1, Riichiro Mizoguchi 2,3 and
Jacqueline Bourdeau 1
1 LICEF Research Center, TELUQ University, Québec, Canada
2 Research Centre for Service Science, Japan Advanced Institute of Science and
Technology (JAIST), Nomi, Japan
3 Laboratory for Applied Ontology (LOA), ISTC-CNR, Trento, Italy
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Workshop Preface: International Workshop on Context
and Culture in Intelligent Tutoring Systems
Valéry Psyché1, Isabelle Savard1, Riichiro Mizoguchi 2,3 and Jacqueline Bourdeau1
1
LICEF Research Center, TELUQ University, Québec, Canada
2
Research Centre for Service Science, Japan Advanced Institute of Science and
Technology (JAIST), Nomi, Japan
3
Laboratory for Applied Ontology (LOA), ISTC-CNR, Trento, Italy cc-
its2018@teluq.ca
With the internationalization of education, the need for adaptation and flexibility in
ITS and other learning systems has never been more pressing, extending to many levels
and fields including: the international mobility of learners, teachers and researchers; the
integration of international, intercontextual and intercultural dimensions in instructional
programs (from primary to higher education and continuing professional development),
as well as in the designs, methods, techniques and tools that support them; the
international mobility of education viewed through the lens of today’s new reality of
mass open online courses accessible by a diverse range of learners around the world
facilitated by ubiquitous, mobile and cloud learning systems. In this sense, there is a
need for more research about context and culture in intelligent tutoring systems.
Teachers and researchers need to develop new adaptation skills and embrace diverse
contexts and cultures as well as leverage this diversity to foster the transfers that can
enhance learning. Clearly therefore, it is important to make room for this diversity in
curricula and learning systems and integrate transfer and adaptation concerns into
pedagogical practice.
But how can we do this concretely? How can we best manage this complexity and
leverage this diversity? How can this materialize in the ITS field, and what are the
benefits?
One of the main focuses of current research is to define the boundaries of context and
culture (C&C) as a theoretical concept and what constitutes the best methods, techniques
and tools in order to collect, analyze and model it from an adaptive learning perspective.
Until recently, C&C modelling was considered an intrinsic part of the various classical
ITS architecture models. Aspects of C&C were therefore partially covered under the
domain, learner, pedagogical and communication models. Now, however, the advent of
big data in education and significant innovations in artificial intelligence are opening
new doors for us to analyze and model C&C differently, if we are able to take advantage
of the information available through the learning analytics process. Big data offers an
exciting opportunity for us to look at C&C modelling for ITS through a new lens. Do
we need a fifth model? Should we view it as another layer in the ITS architecture? Let’s
start thinking about it. In today’s era of adaptive learning delivering anything learners
need, anywhere and at any time, the potential for context and culture-aware ITS could
be huge. What would knowledge representation and reasoning mechanisms look like in
ITS? What kinds of limits might C&C represent for ITS? How can we identify or
measure these limits? Can ocular and biometric measurement play an instrumental role?
What are the logical next steps in terms of conducting studies about context and culture-
aware ITS and gathering and analyzing data about context and culture?
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This C&C@ITS2018 workshop aims to build the foundations of this research stream by
forming an international research community and providing new avenues and questions
for research. New avenues and questions for research may include the following: Will
integrating context and culture mean changing traditional ITS architecture by proposing
new models? Is there any interest in using AI innovations (big data, deep learning) with
the modelling of context and culture knowledge? Why, knowing that there are many
schools of thought? Where do we begin to combine our efforts? Do other modelling
methods such as ontological engineering represent a better way to achieve this goal? Is
it relevant to use AI techniques for education such as educational data mining or learning
analytics to maintain up-to-date knowledge about contextual and cultural diversity?
How can an lTS accommodate and leverage this new complexity to gain awareness of
contextual and cultural diversity? How can earning analytics support contextual and
cultural adaptation, and how can we combine the two? What is the role of the learner in
contextual and cultural adaptation? How can contextual and cultural diversity make
learning deeper and richer?
In light of the above, submissions are welcomed for this workshop on topics
including, but not limited to, the following: Contextual theory; Ontological and
cognitive modelling of contextual or cultural knowledge/context or culture-aware ITS;
Context-aware collaborative learning; Contextual or cultural knowledge in ubiquitous,
mobile and cloud learning systems and various application areas.
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