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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Mon, 18 Apr 2005 15:44:13 +0100
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William Hudson <[log in to unmask]>
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Posted on behalf of Sherman Alpert [mailto:[log in to unmask]] 

Please post to CHI-ANNOUNCEMENTS; Thanks! 
Student Modeling for Language Tutors
Workshop at AIED 2005
12th International Conference on Artificial Intelligence in Education
18-22 July
Amsterdam, The Netherlands

We welcome submissions to the Student Modeling for Language Tutors
Workshop at AIED 2005. 

Topics and goals: 
Student modeling is of great importance in intelligent tutoring and
intelligent educational diagnostic and assessment applications.
Modeling and dynamically tracking a student's knowledge state are
fundamental to the performance of such applications. However, student
modeling in CALL applications differs from more " 
classic" student modeling in other domains in three key ways: 

1. It is difficult to determine the reasons for successes and errors in
student responses.  In classic ITS domains (e.g., math and physics), the
interaction with the tutor may require students to demonstrate
intermediate steps, and there exist heuristics and approaches (e.g.,
model tracing) to determine where a student's problem solving efforts
goes awry.  For performance in language domains, much more learner
behavior and knowledge is hidden, and having learners demonstrate
intermediate steps is difficult or perhaps impossible, and at any rate
may not be natural behavior.  (How) Can a language tutor reason about
the cause of a student mistake? (How) Can a language tutor make
attributions regarding a student's knowledge state based on overt

2. A priori cognitive modeling is harder in language domains.  A
standard approach for building a cognitive task model is to use
think-aloud protocols.  Asking novices to verbalize their problem
solving processes while trying to read and comprehend text is not a
fruitful endeavor.  How then can we construct problem solving models?
Can existing psychological models of reading be adapted and used by
computer tutors? 

3. It may be difficult to accurately score student responses.  For
example, in tutors that use automated speech recognition (ASR), whether
the student's response is correct cannot be determined with certainty.
In contrast, in classic tutoring systems scoring the student's response
is relatively easy.  How can "scoring" 
inaccuracies be overcome to reason about the students' proficiencies? 

Given these differences, a focused workshop bringing together people
working on student modeling in language tutors is appropriate as it
provides a forum to discuss approaches to overcoming these problems. 

This workshop will focus on student modeling for intelligent
computer-assisted language learning (CALL) applications, addressing such
domains as oral reading decoding, and reading and spoken language
comprehension.  Domains of interest include both primary (L1) and second
language (L2) learning. Hence, the workshop will address such questions
- What should a student model for a reading tutor or other CALL tutors
contain? What knowledge components and elements should be maintained? 
- How should information about users be represented? Using what
representational formalisms? 
- With what (cognitive or other) design rationale? 
- How can information about the user's knowledge be obtained (via
interaction with the CALL application) and what sort of inferences can
be made about a student's knowledge based on empirical performance? 
- How, and for what tutor tasks, can the student model be utilized? 
- How can the student model help guide a tutor in terms of instructional
or remedial interventions? In terms of assessment? 
Target audience: 
Researchers and developers of CALL applications that involve student
modeling for intelligent diagnosis, adaptive intervention, and/or
adaptive interaction. 
Call for Papers and Proposals: 
We welcome papers in thefollowing categories: 
- Full papers (up to 8 pages) - Describes work (research, systems) that
involves student modeling for language learning - Position papers (up to
4 pages) - Describes your qualifications, background, and interest with
regard to student modeling for language learning 
      - We also welcome discussion or panel proposals - Demonstrations
(up to 4 pages) - Describes an application or other work to be
demonstrated live at the workshop 

Please contact the workshop chairs by email as soon as possible, briefly
describing your intended submission. 

Important Dates: 
Deadline for paper submission 25 April 2005 Notification of acceptance
11 May 2005 Camera ready version 20 May 2005 Workshop date 18 or 19 July
Sherman R. Alpert
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IBM T.J. Watson Research Center
Phone: +1 914 945 1874 
Joseph E. Beck
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Center for Automated Learning and Discovery Carnegie Mellon University
Phone:  +1 412 268 5726 
Program Committee: 
Jack Mostow
Director, Project LISTEN,
Carnegie Mellon University
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W. Lewis Johnson
Director, Center for Advanced Research in Technology for Education, USC
/ Information Sciences Institute [log in to unmask] 
Stephen A. LaRocca, Ph.D
Army Research Laboratory (ARTI)
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Lisa N. Michaud
Department of Mathematics and Computer Science, Wheaton College
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Peter Fairweather
IBM T.J. Watson Research Center
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