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From: amp@cblelcd.nosubdomain.nodomain.leeds.ac.uk (Ana Maria Paiva)
Subject: CFP: EuroAIED
Originator: amp@cblelcd.leeds.ac.uk (Ana Maria Paiva)
Message-ID: <1995Nov28.135232.3298@leeds.ac.uk>
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Date: Tue, 28 Nov 1995 13:52:32 +0000 (GMT)
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For the latest version of this document please consult    
http://www.cbl.leeds.ac.uk/conferences/EuroAIED

        EUROPEAN CONFERENCE ON AI IN EDUCATION (EuroAIED)
         FUNDACAO CALOUSTRE GULBENKIAN, LISBON, PORTUGAL
                SEPTEMBER 30 - OCTOBER 2, 1996

                       CALL FOR PAPERS

EuroAIED is intended to provide an opportunity for researchers to 
develop principles for the design of systems to support learning.  
The "principles" may be derived from artificial intelligence, 
cognitive science, human-computer interaction, and related fields; 
the "systems" include tutoring systems, learning environments, 
simulations, multimedia systems, WWW-based systems, etc.; and 
"learning" includes learning by schoolchildren, university students 
and industrial trainees.  EuroAIED is intended to fill a gap for 
European researchers caused by the major AI-ED conferences being 
outside Europe from 1993 to at least 1999.  

EuroAIED will be organised as a set of about 18 'working sessions'.  
The usual format of a working session will be: some short 
presentations of submitted papers addressing a set of pre-specified 
questions, followed by a general discussion, and then (in a plenary 
session) a synthesis presentation of the conclusions of the working 
session.  It is intended that there be about 120 participants at 
EuroAIED.  We anticipate having three working sessions in parallel, 
hence 30-50 participants at each.  The questions to be addressed 
by the working sessions are given below.

You are invited to submit a paper for one of these working sessions. 
A good paper will orient a description of your own recent research 
towards answering the specified question(s).  The nature of an 'answer'
is flexible: it could present research outcomes or preliminary
provisional results, or be more speculative (such as a position paper),
or even re-interpret the original question.  All submissions will be 
fully reviewed.  All accepted papers will be published in the 
EuroAIED proceedings.  We anticipate making all the papers available 
on the Web before the conference so that preliminary work towards 
answering the questions may be carried out.

Papers should be no longer than 8 pages or 5000 words.  
A cover page should include an abstract of no more than 400 words, 
give the principal author's affiliation, phone and email address, and 
specify the working session for which the paper is submitted.

Submit the paper to:
	EuroAIED
	Computer Based Learning Unit
	University of Leeds
	Leeds LS2 9JT
	England
Details of the electronic submission procedures have still to be 
defined.  Please consult the WWW page for the up-to-date information:
http://www.cbl.leeds.ac.uk/conferences/EuroAIED

EuroAIED deadlines:
	Submissions due:		February 29th 1996
	Acceptance notification:	April 30th 1996
	Final version due:		May 31st 1996
	Pre-registration deadline:	August 12th 1996

We will endeavour to ensure that EuroAIED provides an informal 
environment for productive interactions.  The Gulbenkian Centre 
provides an excellent location.  We will ensure that registration
fees are as low as possible.  Lisbon itself provides an attractive
environment for informal discussions and an excellent base for a
holiday break before or after the conference.

Organising Committee: 
--------------------
(as of 12/11/95)
Paul Brna, University of Lancaster, England
Ana Paiva, INESC, Portugal
John Self, University of Leeds, England

Program Committee:
-----------------
(as of 12/11/95)
Jean-Paul Almeida, European Virtual University
Michael Baker, CNRS-COAST, Lyon, France
Joost Breuker, University of Amsterdam, Netherlands
Peter Brusilovsky, Int'l Centre for Scientific and Tech. Info., Russia
Stefano Cerri, University of Milan, Italy
Helder Coelho, University of Lisbon, Portugal
Ernesto Costa, University of Coimbra, Portugal
Richard Cox, University of Edinburgh, Scotland
Teresa del Soldato, Open University, England
Pierre Dillenbourg, University of Geneva, Switzerland
Ben du Boulay, University of Sussex, England
Julian Gutierrez, University of the Basque Country, Spain
Ulrich Hoppe, University of Duisburg, Germany
Kim Issroff, University College London, England
Paul Kamsteeg, University of Nijmegen, Netherlands
Alfred Kobsa, GMD, Germany
Collette Laborde, University Joseph Fourier, Grenoble, France
Daniele Nardi, University of Rome 'La Sapienza', Italy
Jean-Francois Nicaud, University of Nantes, France
Reinhard Oppermann, GMD, Germany
Paolo Petta, Austrian Research Institute for AI, Austria
Rachel Pilkington, University of Leeds, England
Rolf Ploetzner, University of Freiburg, Germany
Peter Reimann, University of Freiburg, Germany
Luigi Sarti, Istituto per le Tecnologie Didattiche, Italy
Daniel Schneider, University of Geneva, Switzerland
Jenifer Tennison, University of Nottingham, England
Wouter van Joolingen, University of Twente, Netherlands
Kris van Marcke, Knowledge Technologies N.V., Belgium
Julita Vassileva, University of the Federal Armed Forces, Munich, Germany
Martial Vivet, University of Maine, France
Barbara Wasson, University of Bergen, Norway 
Gerhard Weber, University of Trier, Germany
Radboud Winkels, University of Amsterdam, Netherlands

The Working Sessions:
--------------------
(The names of the session organisers are shown in brackets)

1. Virtual Reality (Jean-Paul Almeida): 
What new problems and solutions does virtual reality provide for 
designers of interactive learning systems?  Is VR the inevitable outcome, 
or the antithesis, of situated cognition?  Is there evidence that VR can be 
used effectively to gain conceptual understanding?  Is the real power of VR 
technology in education the ability to augment reality?

2. Collaborative Learning (Michael Baker, Pierre Dillenbourg):  
Research on collaborative learning shows that the cognitive
effects of collaborative activities depend in part on the nature and
quality of the interaction between learners. Despite a large body of research
on interaction analysis and modelling, understanding natural language
interactions between learners remains a difficult research problem.
Given this situation :
- is there any realistic prospect of a learning system being able to
monitor such interactions in order to make useful educational 
interventions?
If not, does this mean that in computer-supported collaborative learning 
the computer must be just a passive intermediary ?
- to what extent can this work help us to design systems in which
human learners or teachers collaborate with a machine agent?

3. Distributed Cognition (Helder Coelho):  
What are the prospects for applying techniques from distributed
artificial intelligence to support the design of systems for
distance learning and to develop theories of distributed cognition?

4. Machine Learning (Ernesto Costa):
Are any machine learning methods sufficiently robust and cognitively   
valid to enable the development of useful simulated students?  Can the  
cognitive model underlying case-based reasoning be used to design new  
integrated learning scenarios?  Can ML techniques be used to explore  
other aspects of a learning environment besides student modeling? In  
what ways can non-symbolic (e.g. neural nets) or other (e.g. genetic
algorithms) computational learning techniques be used to solve "old"  
learning environment problems?

5. Motivation (Teresa del Soldato, Kim Issroff):  
What is the role of motivation in cognitive processes? Should learning
systems try to model the affective aspects as well as the cognitive aspects
of learning interactions? If so, which affective aspects and how? Or is
there evidence that the novelty of learning systems using new technologies,
without considering affective aspects, is sufficient to enable students to
learn effectively?

6. Qualitative Reasoning (Radboud Winkels):
In the mid-1980s, it was considered that one of the distinctive 
features of intelligent tutoring systems was their ability to perform 
qualitative modelling.  Are qualitative methods of more or less 
relevance to the current generation of learning system?  Are there 
any convincing examples of the application of AI methods for 
qualitative reasoning in learning systems?

7. Student Modelling (Ulrich Hoppe, Rolf Ploetzner):
Are the traditional student modelling techniques, which were 
arguably not much use in intelligent tutoring systems, even more 
useless for modelling and supporting students working collaboratively?
Or may new distributions of roles between humans and model-based 
artificial agents in distributed, group-oriented environments lead
to more convincing forms of intelligent learning support?

8. Cognitive Science (Paul Kamsteeg, Gerhard Weber):
Has recent cognitive science ('recent' meaning 'of the last ten
years or so') contributed anything new to the design of learning systems?
* Are we still using symbolic, rule-based paradigms (ACT, SOAR, etc.) of
the early 1980s? If so, how have they developed vis-a-vis learning? If 
not, what new paradigms have evolved, how are they different in designing
learning systems? Should any of them (or any paradigm at all) be considered
as THE "unified" theory of cognition? If not, should we choose or should we
combine in learning system design?
* What about teaching for insight? After the excitement in the early 80s,
this phenomenon proved to be very elusive. Has recent cognitive science
provided any useful insights about how insight works and how insightful
explanations can be given? What are the relations between insight and 
"deep knowledge", problem conception, viewpoint, learning transfer, 
metacognitive skills?

9. Authoring Systems (Kris van Marcke, Luigi Sarti): 
Can or need AI-based authoring systems
-  allow a domain expert to develop the definition and representation of 
domain,
   content and/or subject matter,
-  facilitate the manipulation of instructional strategies and control,
-  provide the primitives that enable an author to model a student 
according to his/her preferred theory or technique,
-  manage presentational aspects in order to improve the quality of
   the instructional communication
-  reduce development costs and improve re-usability by sharing knowledge
using a knowledge communication language such as KIF?
How is the AI situated at the authoring level, and how is it situated
at the tutoring level?

10. Metacognition (Daniele Nardi):
Will the AI concepts of meta-reasoning and meta-knowledge help
us to design systems which support the development of metacognitive
skills?

11. External Representations (Richard Cox):
It has been convincingly argued that external representations have a 
number of key cognitive effects and semantic properties.  Can systems be 
designed to provide the potential benefits of multiple representations?  
Should training be guided by an explicit curriculum for the selection, 
construction and use of external representations?  How should individual 
differences in cognitive style be addressed?  Should all students be taught 
the same external representation curriculum or should they be encouraged to 
follow their representational modality preferences?  From a constructivist 
perspective, what roles are played by the process of externalising one's 
cognition in the course of reasoning with external representations?  Are we 
yet in a position to issue principled guidelines to multimedia developers, 
instructional designers etc regarding the assignment of information to 
representational modalities?

12. Natural Language (Rachel Pilkington, Stefano Cerri):
Are the methods developed for the analysis of natural language 
dialogues (e.g. dialogue game theory and rhetorical structure theory) 
likely to be of much use for managing educational interactions with 
new technology-based systems?

13. Self-explanation (Peter Reimann):
Self-explanation is now established as a cognitive process
beneficial to learners. How can systems be designed to cause it to
happen?  Which other, comparable learning strategies should be fostered
by systems, and how? (e.g., working methods for discovery environments,
search methods for complex information systems,...).

14. Internet-Based Learning Environments (Daniel Schneider, Jenifer Tennison,
Paolo Petta):
How can we design and implement effective networked systems to 
support (1) enhanced educational hypertext, (2) interactive shared learning
environments and (3) human-computer collaboration? In particular,
can we design WWW and MUD-based systems that include interactive,
collaborative and adaptive features; interface and information agents;
tutoring and "learning-by-doing" components?

15. Educational Multimedia (Julian Gutierrez, Alfred Kobsa):
What are the main contributions and problems of multimedia
information and techniques in intelligent educational systems?
What are the repercussions of the integration of multimedia
techniques and information in the traditional architecture of
educational systems (student model, knowledge representation, etc.)?
What are the main contributions and problems which arise from the use of
AI techniques in educational multimedia systems?
May educational multimedia systems be built without using AI?

16. Social Learning (Martial Vivet, Reinhard Oppermann): 
What differences result when learning systems are designed 
in and for the workplace, rather than in a research laboratory?  
What, precisely, are the different principles of user-participatory 
design?  Is there any evidence that the resulting systems are more 
effective?  What are the prospects for supporting learning on the
job through educational systems?  How can social learning in
workgroups be supported?  How can the consultation of local or
central experts be improved?  How can the process of learning be
supported (re-use of knowledge)?

17. Instructional Planning (Barbara Wasson, Julita Vassileva):
Whatever happened to instruction?  What kind of instruction 
(if any) is needed in constructivist learning environments?  How 
should instruction be planned in such environments, or is this a 
self-contradiction?

18. Simulation-based learning (Wouter van Joolingen):
Simulation based learning technology is expected to play an increasingly
important role for the development of industrial related training systems. 
Researchers in simulation-based learning have reached a consensus that 
adaptive support to the learner is required in order to maximise the learning gains. 
.  How much of the learning support can be built in simulation based
learning systems and how much must remain with the human tutor? 
.  What is the cost of providing explanations in a simulation context? 
.  Should the student evaluation be cognitive or performance based? 
.  How much of ITS technology can we reuse? 
.  What is the status of authoring shells for training simulations? 

19. Other Innovations (Paul Brna, Ana Paiva):
Are there any innovative areas (other than those referred to in
the previous questions) which we can anticipate will have a 
significant impact on the design of learning systems in the short
to medium term?






Keywords: 

