The conference is sponsored by the AI-ED Society of the Association for the Advancement of Computing in Education (AACE), a non-profit international organization, and publisher of the Journal of Artificial Intelligence in Education, and is supported by the leading organizations in the field.
Intelligent Learning Environments for Programming:
The Case for Integration and Adaptation
PETER BRUSILOVSKY
ICSTI, Russia
From Case-Based Reasoning to
Scaffolded Electronic Notebooks: A Journey
JANET L. KOLODNER
Georgia Institute of Technology, USA
Design and Adaptive Interactions:
Two Levels of Intelligence in Social Learning Systems
TAK-WAI CHAN
National Central Univ., Taiwan, R.O.C.
Diagnosis Is Mutual: A Distributed Cognition Approach
PIERRE DILLENBOURG
Univ. of Geneva, Switzerland
Discourse Generation for Instructional Applications:
Making Computer-Based Tutors More Like Humans
JOHANNA D. MOORE
Univ. of Pittsburgh, USA
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| AI-ED 95 OVERVIEW |
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| Tuesday, August 15 |
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| Registration 4 PM-8 PM |
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| Wednesday, August 16 |
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| Registration 8 AM-8 PM |
| Tutorials/Workshops 8:30 AM-5 PM |
| Welcome Reception 6 PM-8 PM |
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| Thursday, August 17 |
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| Registration 8 AM-5 PM |
| Opening of Conference 8:30 AM-9 AM |
| Invited Talk-Louis Gomez 9 AM-10 AM |
| Break 10 AM-10:30 AM |
| Paper Sessions 10:30 AM-12:10 PM |
| Invited Talk-Peter Brusilovsky 1:50 PM-2:50 PM |
| Break 2:50 PM-3:20 PM |
| Paper Sessions 3:20 PM-5 PM |
| Posters & Dessert Reception 7:30 PM-9:30 PM |
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| Friday, August 18 |
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| Registration 8 AM-5 PM |
| Invited Talk-Janet Kolodner 8:30 AM-9:30 AM |
| Break 9:30 AM-10 AM |
| Paper Sessions 10 AM-11:40 AM |
| Invited Talk-Tak-Wai Chan 1:20 PM-2:20 PM |
| Paper Sessions/Panel 2:20 PM-3:35 PM |
| Break 3:35 PM-4 PM |
| Panel Session 4 PM-5 PM |
| AI-ED Society Business Meeting 5 PM-5:30 PM |
| Potomac River Dinner Cruise 7 PM-11:30 PM |
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| Saturday, August 19 |
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| Registration 8 AM-5 PM |
| Invited Talk-Pierre Dillenbourg 8:30 AM-9:30 AM |
| Break 9:30 AM-10 AM |
| Paper Sessions 10 AM-11:40 AM |
| Paper Sessions 1:20 PM-3 PM |
| Break 3 PM-3:20 PM |
| Invited Talk-Johanna Moore 3:20 PM-4:20 PM |
| Closing Session & Reception 4:20 PM-5PM |
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Prequisite Knowledge: A general familiarity with issues in cognitive science would definitely be helpful if not absolutely necessary. If the participants are to then go on and use the ACT-R system, an ability in LISP programming would again be very helpful if not necessary.
This tutorial explores the dual tutoring/life long learning roles of critics and how they may be implemented in previously existing automated environments. We will survey how critics are being embedded within environments such as word processing, hospital systems, CAD packages, command and control workstations, software programming languages, and smart appliances. We will give special attention to personalised learning environments and daily knowledge alerting systems in information highway settings. There will be hands-on interaction with several working critics, a life long learning library, and with an application programming shell.
Intended audience: Practitioners interested in a how-to-do-it lecture on critiquing and life long learning approaches they can embed in environments they are interested in. Researchers interested in improving on-demand or situated life long learning approaches should also find the tutorial beneficial. No previous knowledge of critics or the information highway is required.
Organizing Committee:
Tom Murray, Univ. of Massachusetts, USA
Charles Bloom, Applied Research & Multimedia Services, US WEST
Technologies, USA
Abstract: As numerous research groups around the world are refining the art
of building intelligent tutors, and several have proven to be quite
effective, there is a growing consensus among researchers that more should
be available in the form of authoring tools that alleviate the need to
start from scratch with each new system. In addition, instructional
designers are in need of usable tools for rapidly prototyping sophisticated
instructional systems. Also, educational theorists are in need of tools
for evaluating educational strategies and alternative curriculum models.
There is still a significant gap between CBT authoring tools such as
Authorware and Icon Author, which have the large and established user base,
and the research tools built in universities for constructing ITSs. This
workshop will provide an opportunity for practitioners and researchers
doing work with ITS shells and CAI authoring tools to share their visions
and concerns regarding the next step in ITS authoring tools. Participants
will make brief presentations, following which there will be general
discussions addressing the following issues related to ITS authoring tools:
usability, evaluation, domain independence, minimalist approaches, multiple
teaching strategies, student models.
Intended Audience: Those doing research in instructional theory who are interested in automated instruction; those doing research with the goal of producing general instructional systems; those interested in the evaluation of ITS; and those applying any type of computer based instruction in a classroom.
If you are interested in participating, please send a position paper or inquiry by May 30th to:
Dr. Tom Murray LGRC A301-A, Computer Science Dept. Univ. of Massachusetts Amherst, MA 01003, USA tmurray@cs.umass.edu The paper should both mention your position on some of the issues and briefly describe previous work that has provided experience leading to these positions. The paper should have no more than 2500 words in the body. No limit to pictures and figures. Include a condensed bibliography of your past work (no more than 15 items long). Though the workshop will focus on key general issues, and discourage detailed summaries of particular systems, we also want to emphasise concrete examples of idea s over vague philosophical positions, whenever possible, and the position papers should briefly describe these.
Organizing Committee:
Ulrich Hoppe, Duisburg, Germany
Sherman Huang, Calgary, Canada
Ana Paiva, Lisbon, Portugal
Abstract: Recently, the AI-ED field has moved away from its roots in AI to consider broader educational, psychological, and social issues. Such considerations should be complemented by the kind of theoretical rigour common in other areas of applied AI. Computational Mathetics is a term invented to denote the study of learning and teaching using the technical, formal concepts of AI. The purpose of Computational Mathetics is to enable components of AI-ED systems to be designed by analytic means (eventually). Recent work on multi-agent systems promises a foundation for Computational Mathetics, since AI-ED systems are essentially concerned with interactions between various agents which may be ascribed various mental attitudes. Other relevant areas of formal AI include belief revision, diagnosis, dialogue, machine learning, meta-reasoning, and nonmonotonic reasoning.
The main aim of this workshop is to review the extent to which formal AI can begin to provide an analytic basis for AI-ED systems and to identify those areas where progress is most likely or urgent. The workshop will be organised as a set of sessions each with a panel discussion based on a review paper.
The workshop is intended for those who have made, or hope to make, some contribution to Computational Mathetics. They would be able to relate some area of formal AI to Computational Mathetics. Those wishing to participate should submit a short position paper by the May 30th to the workshop chair outlining their experiences or views on some aspect of Computational Mathetics.
Organizing Committee:
Koen Bertels, FUNDP, Namur, Belgium
Bart De Decker, KULAK, Kortrijk, Belgium
Diana Bental, Middlesex Univ., Bounds Green Road, London N11 2NQ, UK
Rudi Lutz, School of Cognitive and Computing Sciences, Sussex Univ., UK
Lewis Johnson, Information Sciences Institute, Univ. of Southern California
Abstract: Techniques for the automated analysis of the semantics of programs have a number of interesting applications within programming education. For example, such tools could be integrated within an ITS for programming in order to improve student modelling capabilities, or they could be used to improve tools for the visualisation of programs, etc.
A lot of research has been done on the automation of novice program analysis. This has resulted in a number of different approaches each with its own vocabulary, its own knowledge representation, and its own techniques.
The purpose of this full-day workshop is to put these different approaches together in order to find out what they have in common and what the most important differences are. Additionally, this should enable a proper comparison of the strengths, weaknesses, and possible applications of each approach. Finally, the workshop hopes to clarify the different vocabularies used by the different approaches.
Intended Audience: The workshop is primarily intended for people who are active in the field of program analysis. However, people working on programming environments and tools should also find this workshop useful.
People wanting to participate in this workshop should submit a position paper in which they describe their own approach to novice program analysis. This should be done using a predefined format, handling specific aspects of their approach (purpose, terminology, knowledge representation, techniques used, handling of erroneous programs, scalability, evaluation, flexibility, and existing approaches). People who have not been working on program analysis should clarify their viewpoints on program analysis and the possible applications.
Detailed guidelines on the format of the position paper can be obtained by e-mail from the Workshop Chair. Position papers should be sent to the Workshop Chair by May 30th.
Bert Bredeweg
Dept. of Social Science Informatics
Univ. of Amsterdam
Roetersstraat 15
1018 BW Amsterdam, The Netherlands
Phone: +31 20 525 6788; Email: bert@swi.psy.uva.nl
Organising Committee:
Ken Forbus, Northwestern Univ., USA
Elliot Soloway, Univ. of Michigan, USA
Barbara White, Univ. of California at Berkeley, USA
Abstract: A large part of the research on qualitative reasoning in AI originated from efforts trying to cope with the limitations that followed from using quantitative simulators for teaching purposes (e.g., SOPHIE and STEAMER). Nowadays qualitative reasoning is an important research area within AI. During the past 10 years the qualitative reasoning community has begun to understand the crucial aspects relevant to this type of reasoning. Many promising results have been achieved, whereas at the same time the limitations of the current techniques are well understood. However, the use of qualitative reasoning techniques within interactive (computer based) learning environments has not been given as much attention as one would have expected.
In this workshop we want to focus on the use of QR techniques in Interactive Learning Environments. People interested in participating in this workshop should submit a 2-4 page position paper on any topic within this theme, including (but not limited to):
Facilitating intelligent tutoring technology transfer
Charles P. Bloom, A. Scott Wolff, Alan Lesgold, & R. Bowen Loftin, U.S.
West Technologies, Univ. of Pittsburgh, Univ. of Houston, USA
A formulation of auxiliary problems and its evaluations
Tsukasa Hirashima, Akihiro Kashihara & Jun'ichi Toyoda, Osaka Univ., Japan
A generative learner model in the domain of second language learning
Sue Sentance & Helen Pain, Univ. of Cambridge & Univ. of Edinburgh, UK
A goal-centred architecture for intelligent tutoring systems
Jim Reye, Queensland Univ. of Technology, Australia
A multi-agent approach to model student reasoning process
Stephane Leman, Sylvain Giroux & Pierre Marcenac, Univ. de La Reunion,
France & Tele Univ., Canada
A shell for intelligent tutoring systems
Bettina Reinhardt & Stefan Schewe, Univ. of Wuerzburg & Bruderkrankenhaus
St. Josef, Germany
AdventurePlayer: Macrocontext plus microworlds
Thad Crews, Gautam Biswas, Mitchell Nathan, Sashank Varma, Susan Goldman &
John Bransford, Vanderbilt Univ., USA
An ITS to plan inquiry dialogue
Lung-Hsiang Wong, Chee-Kit Looi & Hiok-Chai Quek, Nanyang Technological
Univ., Singapore
An adaptive hypermedia system
Tomas A. Perez, Julia N. Guierrez & Philippe Lopisteguy, Univ. del Pas
Vasco, Spain
An authoring component for protocol driven hypertext explanations
Markus Lusti, Basel Univ., Switzerland
An intelligent tutoring system for Japanese interpersonal expressions
Kyoko Kai & Jun-ichi Nakamura, Kyushu Inst. of Technology, Japan
Application and development of multiple teaching styles
to an engineering ITS
Chaisak Srisethanil & Nelson Baker, Georgia Inst. of Technology, USA
Automated video assessment of human performance
Andrew S. Gordon, Northwestern Univ., USA
Automatic generation of tutors for spreadsheet applications
Maurizio Lentini, Daniele Nardi & Alessandro Simonetta, Univ. di Roma
"La Sapienza", Italy
Belvedere: Engaging students in critical discussion of science and
public policy issues
Daniel Suthers, Arlene Weiner, John Connelly & Massimo Paolucci, Univ. of
Pittsburgh, USA
CAL language: When education influences the design of an AI language
Adil Kabbaj & Claude Frasson, Univ. de Montreal, Canada
CONNIE: An interactive learning environment for creative tasks based on the
negotiation of constraints
Matt Smith, King Alfred's College of Higher Education, UK
Cognitive conceptual models for defining robot control
David Mioduser, Ilya Levin & Vadim Talis, Tel-Aviv Univ., Israel
Cognitive diagnosis revisited
Kees de Koning, Joost Breuker & Bert Bredeweg, Univ. of Amsterdam, The
Netherlands
Conceptual graphs for similarity measurement in a case-based physics
problem solver
Pak-Wah Fung & Alison Adam, Univ. of Manchester, UK
Contribution to studying negotiation: A knowledge items approach
Pierre Jambaud & Daniele Herin-Aime, LIRMM, France
Design and implementation of simulation-based discovery environments:
The SMISLE solution
Wouter van Joolingen & Ton de Jong, Univ. of Twente, The Netherlands
"Device Models" in student modeling
Yury V. Tsybenko, Glushkov Inst. for Cybernetics, Ukraine
"Did I say what I think I said, and do you agree with me?":
Inspecting and questioning the student model
Susan Bull & Helen Pain, Univ. of Edinburgh, UK
Dynamic case-based tutoring: A cognitive science approach
Thomas J. Schult & Peter Reimann, Univ. of Freiburg, Germany
Enabling abstractions: Key steps in building physics models
Diana Bental & Paul Brna, Equipe COAST & Lancaster Univ., UK
Externalising learner models
A. Paiva, J. Self & R. Hartley, Technical Univ. of Lisbon, Portugal; Univ.
of Lancaster & Univ.
Graphic interface for parallelism in educational robotics
Jean-Baptiste La Palme & Maurice Belanger, Univ. of Quebec, Canada
How to elicit self-explanation
Ahihiro Kashihara, Tsukasa Hirashima & Jun'ichi Toyoda, Osaka Univ., Japan
Integrating intelligent assistants in an educational hypermedia system
Neide Santos, Carlos A.N. Cosenza & Ana Regina Rocha, Univ. Federal do Rio
de Janeiro, Brasil
Intellectual skills and cognitive strategies: Can one method tutor both?
Frank Linton, The MITRE Corporation, USA
Intelligent tutoring goes to school in the big city
Kenneth R. Koedinger, John R. Anderson, William H. Hadley & Mary Mark,
Carnegie Mellon Univ., USA
Knowledge construction and sharing in quorum
Alberto J. Canas, Kenneth M. Ford, John Brennan, Thomas Reichherzer & Pat
Hayes, Univ. of West Florida & Univ. of Illinois, USA
Knowledge decomposition and subgoal reification in the
ACT programming tutor
Albert T. Corbett & John R. Anderson, Carnegie Mellon Univ., USA
Learner adaptivity in generic instructional strategies
Kris Van Marcke & Henriette Vedelaar, Knowledge Technologies N.V., Belgium
Learner-centered design of sensorily immersive microworlds using a virtual
reality interface
Marilyn C. Salzman, Chris Dede & R. Bowen Loftin, George Mason Univ.
& NASA/Johnson Space Center, USA
Learning environments for conceptual change: The case of statistics
Geoff Cumming & Neil Thomason, La Trobe Univ. & Univ. of Melbourne,
Australia
Missing opportunities for learning in collaborative problem-solving
interactions
Michael Baker & Katerine Bielaczyc, Ecole Normale Superieure de Lyon,
France
Model progressions and cognitive flexibility theory
Julie-Ann Sime, Lancaster Univ., UK
Modeling hypermedia navigation: An AI approach
Chuen-Tsai Sun, Yu-Tai Ching & Fu-Xiong Lin, National Chiao Tung Univ.,
Taiwan
Modelling and mending student's misconceptions in translating algebra word
problems using a belief revision system in TAPS
Normaziah Aziz, Helen Pain & Paul Brna, Univ. of Edinburgh, UK
Multiviews learning and intelligent tutoring systems
M. Quafafou, A. Mekaouche & H.S. Nwana, IRIN, France & Univ. of Keele, UK
On automatic generation of intelligent tutoring systems
Ruqian Lu, Cungen Cao, Yunhong Chen & Zhanggang Han, Academia Sinica,
NCIC & Hua Qiao Univ., China
Ontological issues of CSCL systems design
Mitsuru Ikeda, Heinz Ulrich Hoppe & Riichiro Mizoguchi, Osaka Univ., Japan
Persistent collaboration: Marrying the technology push with the
learning pull
Tom Conlon & Helen Pain, Heriot-Watt Univ. & Univ. of Edinburgh, UK
Prerequisite relationships for the adaptive assessment of knowledge
Cornelia E. Dowling & Rainer Kaluscha, Technische Univ. Braunschweig,
Germany
Presenting examples in explanations: A preliminary study of some textual
factors on comprehension
Vibhu O. Mittal, Univ. of Pittsburgh, USA
Probabilistic approach to adaptive students' knowledge assessment:
Methodology and experiment
Valery Petrushin, Katherine Sinitsa & Victoria Zherdienko, Georgia Inst. of
Technology, USA & Glushkov Inst. of Cybernetics, Ukraine
Providing examples and individual remindings in an intelligent programming
environment
Gerhard Weber, Univ. Trier, Germany
Putting intelligent tutoring systems technology into practice
Charles P. Bloom, Randall Sparks, Scott Dooley, Lori Meiskey, Brigham Bell
& Anne McClard, US West Technologies, USA
REDEEM: Creating reusable intelligent courseware
Nigel Major, Univ. of Nottingham, UK
Reactive instructional planning to support interacting teaching strategies
Julita Vassileva, Federal Armed Forces Univ., Germany
Reciprocal-tutoring-kids: Tutor-tutee role playing systems
T.W. Chan, C.Y. Chou, M.F. Lee & M.H. Chang, National Central Univ., Taiwan
Representing mathematical problem solving episodes with an evolving student
model
Denise Gurer, SRI International, USA
SMART evaluation: Cognitive diagnosis, mastery learning and remediation
Valerie J. Shute, Lackland Air Force Base, USA
Suggesting multiple design actions using prior cases
Agustin A. Araya, San Jose State Univ., USA
Supporting the reengineering of corporate training
Charles P. Bloom & A. Scott Wolff, US West Technologies, USA
The MR Tutor: Computer-based training and professional practice
M. Sharples, J.B.H. du Boulay, B.A. Teather, D. Teather, N. Jeffery, & G.H.
du Boulay, Univ. of Sussex, De Montfort Univ. & Inst. of Neurology, UK
The use of multiple student modeling to parameterize group learning
H. Ulrich Hoppe, GMD-IPSI, Germany
Towards an epistemology of intelligent problem solving environments:
The hypothesis testing approach
Claus Moebus, C.v.O Univ., Germany
Towards lightweight tutoring agents
Steven Ritter & Kenneth R. Koedinger, Carnegie Mellon Univ., USA
Using a simulated student for instructional design
Joseph S. Mertz, Jr. & Jill H. Larkin, Carnegie Mellon Univ., USA
Using hypertext for an adaptive helpsystem in an intelligent
tutoring system
Meike Gonschorek & Christian Herzog, Technische Univ. Munchen, Germany
Validating a 2D framework of qualitative and quantitative models for an ITS
Wee-Chee Sim, Chee-Kit Looi & Hiok-Chai Quek, Nanyang Technological Univ.
& Information Tech. Inst., Singapore
When less is more: Supporting authoring and interface building via
special-purpose task models
Benjamin Bell & Smadar Kedar, Northwestern Univ., USA
A blackboard approach to a knowledge based tutoring system for linear
programming
Andreas Born & Markus Lusti, Inst. fur Informatik, Switzerland
A framework for building agent based learning environments
Pentti Hietala, Univ. of Tampere, Finland
A framework for intelligent tutoring systems (ITS)
Binghui Helen Wu, Lehigh Univ., USA
A microworld for mathematics as a finite automaton: The case of the design
of a learning environment for the discovery of an algorithm for addition of
two-digit numbers
Martin J. Ippel, Leiden Univ., The Netherlands
A multi-subject intelligent student assessment system
Eric Foxley & Bill Lou, Nottingham Univ., UK
A student modeling technique for problem solving in domains with large
solution spaces
Cristina Conati & Kurt VanLehn, Univ. of Pittsburgh, USA
A tutoring architecture that learns
R. Morelli, B. Dinkins & G. Pelton, Trinity College & Carnegie Mellon
Univ., USA
APT: A programming tutor for experienced programmers
Vikki Fix & Susan Wiedenbeck, Univ. of South Dakota & Univ. of Nebraska,
USA
Alexia: A computer-based environment for French foreign language lexical
learning
Thierry Chanier, Nathalie Cointe, Christophe Fouquere & Fabrice Issac,
Univ. Clermont-Ferrand & Univ. Paris-Nord, France
An aiding tool for instructional systems generation: Requirements
Ana Arruarte, Isabel Fernandez-Castro & Begona Ferrero, Univ. of the Basque
Country, Spain
An automatic verifier for calculus exercises at the university level
Laura Farinetti, Pier Luca Montessoro & Anna Rosa Scarafiotti, Politecnico
di Torino & Univ. di Udine, Italy
An embedded reasoning agent for skill acquisition
Chaochang Chiu & A.F. Norcio, Yuan Ze Inst. of Technology, Taiwan ROC;
& Univ. of Maryland, USA
An intelligent tutoring system for quality control in the food industry
Oscar Castillo & Patricia Melin, Inst. Technolgico de Tijuana & CETYS
Tijuana, Mexico
Communicative language learning with PROMISE
Petra Ludewig & Friedrich Kronenberg, Univ. Osnabruck, Germany
DEVICE: Dynamic Environment for Visualization in Chemical Engineering
Noel Rappin, Mark Guzdial, William Ernst, Peter Ludovice, Matthew Realff
& Dennis Senol, Georgia Inst. of Technology, USA
Design of a general planmatcher for diagnosing student programs
Bedir Tekinerdogan, Hein P.M. Krammer & Jeroen J.G. van Merrienboer, Univ.
of Twente, The Netherlands
Designing the instructor and the student model using a model of the
activity
Leila Alem, Macquarie Univ., Australia
Developing skill-specific help for adaptive feedback in a discovery
learning environment
Merryanna Swartz & Michael Steib, Vitro Corp., USA
Development of a discovery learning tutoring system construction
environment
Shamus Smith & R.H. Kemp, Massey Univ., New Zealand
Education in mathematics: A reasoning apparatus for proofs by contradiction
Attilio Colagrossi & Alessandro Micarelli, IASI-CNR & Terza Univ. di Roma,
Italy
Expert piano: An intelligent tutoring system-based educational environment
Jose Honorio Glanzman, Ana Regina Rocha & Neide Santos, Federal Univ. of
Rio de Janeiro, Brazil
FEBL: Fuzzy Explanation-Based Machine Learning
Ying-Chun Li, Beijing Normal Univ., China
>From a real world learning experience to a learning environment
Hyacinth S. Nwana & Mohamed Quafafou, Univ. of Keele, UK
ITS authoring tools: The next generation
Stephen B. Blessing, Carnegie Mellon Univ., USA
Instructional sequencing in intelligent tutoring systems? Taking cues from
experts' internalized knowledge structures
Sherrie P. Gott & Robert A. Pokorny, U.S. Air Force Armstrong Laboratory,
USA
Intelligent assistance through graphical symptoms
Alice T. Cybis Pereira, Univ. Federal de Santa Catarina, Brazil
Intelligent multimedia systems on WWW for fill-in-the-blank program
problems
Masato Soga, Akihiro Kashihara & Jun'ichi Toyoda, Wakayama Univ. & Osaka
Univ., Japan
Interactive visual guidance learning environment
Jih-Shih Hsu, National Yunlin Inst. of Technology, Taiwan
Knowledge-based assistance in marking scheme specification and answer
script evaluation
Narasimha Bolloju, City Univ. of Hong Kong, Hong Kong
Making computer-supported collaborative learning systems truly supportive
Sandra Katz, Univ. of Pittsburgh, USA
Model construction for training case solving in law
Antoinette Muntjewerff & Radboud Winkels, Univ. van Amsterdam,
The Netherlands
Modelling reflection in teaching-learning dialogues
John Cook, Thames Valley Univ., UK
Modelling the user module of an intelligent computer assisted learning
system: A constructivist approach
Vania Ribas Ulbricht, Alice T. Cybis Pereira, Raul S. Wazlawick & Neri dos
Santos, Univ. Federal de Santa Catarina, Brazil
More errors please: Interlanguage errors as a positive sign of learning
Jenifer Burckett-Picker & Ethel Schuster, Simmons College, USA
Multilanguage technology in Pythagoras software
Vladimir V. Prokhorov, Russian Academy of Sciences-Ural Branch, Russia
Personalized learning environment on the knowledge of a community
Akira Namatame, National Defense Academy, Japan
Pop class intelligent tutoring systems: Shell, toolkit & design technology
Vladimir A. Goodkovsky & Edward V. Kirjutin, Moscow State Inst. for Physics
& Engineering, Russia
ProGen: A tool for teachers
Igor Shevchenko, Far Eastern State Univ., Russia
Problem solving models for motivation, learning and teaching
Alexei M. Dovgiallo, Glushkov Inst. for Cybernetics, Ukraine
Shadow: Adaptation of the tutoring interaction to the changing interests of
the student
M. Quafafou, A. Mekaouche & K.J. Mock, IRIN, France & Univ. of California-
Davis, USA
Smart Tools: A multi-representational approach to teaching functional
relations
Stephen Owens, Gautam Biswas, Mitchell Nathan, Linda Zech, John Bransford &
Susan Goldman, Vanderbilt Univ., USA
The intelligent learning support system on the distributed cooperative
environment
Toshio Okamoto, Akiko Inaba & Yasutaka Hasaba, Univ. of Electro-
Communications, Japan
Towards an intelligent tutorial system in differential equations
Constanza R. Huapaya & Graciela M. Arona, Univ. Nacional de Mar Del Plata,
Argentina
Towards meta-learning tools for mechanics
Mohammad Sapiyan & Hyacinth S. Nwana, Keele Univ., UK
Towards new learning strategies in intelligent tutoring systems
Esma Aimeur, Claude Frasson & Carmen Alexe, Univ. de Montreal, Canada
Turning a CBT into an ITS
Carol L. Redfield & Tandy Herren, Univ. of Texas-San Antonio & Medical
Science Systems, USA
Tutoring effective problem-solving strategies in debugging
Byung-do Yoon & Oscar N. Garcia, George Washington Univ., USA
Understanding students' solutions in SYPROS
Christian Herzog, Technische Univ. Munchen, Germany
Using computers to improve reading skills: Recommendations for an ITS
Cathy Lewin, Open Univ., UK
Utilization of systems modelling of knowledge base representation for
science instruction
Yuri Orlik, Pontificia Univ., Colombia
What pedagogical structure for an educational hypermedia?
J.B. Dubois & P. Prevot, Laboratoire d'informatique des Systemes de
production Industriell, France
This will be held for participants to make new acquaintances and meet with friends.
In conjunction with the evening Poster sessions, attendees are invited to a dessert reception featuring gourmet coffees and teas and an assortment of pastries and sweets.
Washington DC and its monuments by night is a sight not to be missed! Enjoy a delicious buffet meal on a luxury cruiser and the convivial company of fellow delegates and guests. The atmosphere will be one of relaxation with music on the climate controlled enclosed deck and on the open deck under the stars. The cruise includes dinner, wine, beer and soft drinks for the duration of the cruise.
Conference participants are invited to the rooftop floor of the hotel to participate in the Closing Session and Reception and to view the spectacular DC skyline. Paper prizes will be awarded and the next conference, AI-ED 97, will be announced.
Tour some of the most famous sites in the heart of the US capital city, including the White House, the Capitol Building, and the Supreme Court. Observe all three branches of the government--Legislative, Executive, and Judicial--in action. Then enjoy the Smithsonian Museum of American History with exhibits of popular American culture--everything from Hollywood memorabilia to the original Star Spangled Banner.
Spend the morning immersed in US history in some of the country's best museums on this tour of Ford's Theatre (including the Lincoln Museum) and the Smithsonian Air & Space and Natural History Museums. Famous exhibits include the President's Box at Ford's Theatre, where President Abraham Lincoln was assassinated; an Air & Space Museum exhibit of Moon Rocks and an Apollo space capsule; and Natural History Museum exhibits of everything from dinosaurs to the Hope Diamond.
Explore the world's largest museum complex, The Smithsonian Institution, with its over 14 museums including the Air and Space Museum, American History Museum, Natural History Museum, National Gallery of Art, Museum of American Art, and National Zoo. You also may want to visit the Library of Congress, National Archives, Capital Children's Museum, National Geographic Society, and National Aquarium. In addition, Washington offers many major theaters, restaurants, and large shopping malls--the closest is the Pentagon City Mall near the conference hotel.
To obtain the best airfare discounts and flights, AI-ED 95 has selected United Airlines as the official carrier. United offers attendees 10% discount off unrestricted coach fare and 5% discount off the lowest applicable fares. When making your reservations or using the services of a travel agent, please use the AI-ED 95 meeting ID number.
Call United Airlines: 1-800-521-4041 (U.S. phone #)
Meeting ID# 590YY
Located on a high elevation across the Potomac River from Washington, DC, the hotel overlooks DC's famous landmarks. The Sheraton National is blocks from the Pentagon, DC Metro train stop and 5 minutes from downtown Washington, the monuments, Kennedy Center, Smithsonian Institution, Air & Space Museum and many more attractions.
Many hotel rooms have a spectacular view of the Washington, DC skyline and monuments. The Sheraton National is easily accessible to the District of Columbia, National Airport and Metro. The hotel provides complimentary shuttle van service to and from Washington National Airport, Metro stop, and Pentagon City Mall, and has indoor (free) parking for cars.
Roommate Service: If you wish to share a room at the conference hotel, please contact the AI-ED Society/AACE, and we will assist with these arrangements.
To receive these special rates, hotel reservations must be made by July 15th and you must identify yourself to the hotel as an AI-ED 95 attendee.
To make your hotel reservation, contact by July 15th:
Sheraton National Hotel Columbia Pike & Washington Blvd. Arlington, VA 22204 800-468-9090 or 703-521-1900, Fax: 703-521-2122
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* A I - E D 95 *
* *
* 7TH WORLD CONFERENCE ON ARTIFICIAL INTELLIGENCE IN EDUCATION *
* *
* August 16-19, 1995 * Washington, DC *
* *
* REGISTRATION FORM *
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Last Name: ___________________________First Name: _______________________
Address: ________________________________________________________________
Address: ________________________________________________________________
City/State/Code: ________________________________________________________
E-mail: _________________________________ Phone: ________________________
__________________________________________________ Fax: _________________
Affiliation for badge (if different from above)
Conference Registration: (U.S. Dollars)
Includes entry to all invited, paper, panel, poster, and
workshop sessions, Potomac River Dinner Cruise, three receptions,
morning and afternoon refreshments, and Proceedings.
Received After
June 30th June 30th
--------- ---------
AI-ED Society Member $335 $385 $ ____
Nonmember $385 $445 $ ____
Student Member $165 $195 $ ____
Student Nonmember * $195 $225 $ ____
* To qualify for the student rate, registration form must
include a dept. letter attesting to full-time status.
AI-ED Society/AACE Membership:
Join the AI-ED Society/AACE now and $65 $ ____
register at the member rate
(non-U.S. add $10 postage)
(See benefits below.)
Tutorials: **
Half-day: T1: ___ T2: ___ $75 $95 $ ____
Workshops: ** (check one)
Full-day: W1: ___ W2: ___ W3: ___ W4: ___ $ FREE
Social Program:
___
|___| Check here if you plan to take the Dinner
Cruise, Friday 7 PM-11:30 PM. Guest
tickets may be ordered below.
Dinner Cruise (guests): Qty: ___ @ $55 $_____
Receptions (guests) Qty: ___ @ $25 $_____
Tourist Excursions:
Highlights of Washington: Qty: ___ @ $25 $_____
The Museums: Qty: ___ @ $25 $_____
Extra Proceedings:
1995 Proceedings: ___ copies @ $45 $_____
TOTAL: $ ______
** Conference registration required.
Roommate Service:
If you wish to share a room at the conference hotel with another
AI-ED 95 attendee, please contact the AI-ED Society/AACE at the
addresses below, and we will try to make these arrangements.
AI-ED Society/AACE Membership:
Membership entitles you to receive--
The AI-ED Society (its 25 member Executive Committee represents 13 countries) seeks to support AI in Education developments throughout the international community. The AI-ED Society is a society of the non-profit, international Association f or the Advancement of Computing in Education (AACE).