Student Modeling for Language Tutors, Workshop at AIED 2005

The following is from the ACM SIGCHI interest list.

At first blush, it would seem that the models and methods
discussed in this workshop would be usable in AT for
those with reading problems.

Al

-- original message

Posted on behalf of Sherman Alpert [mailto:salpert@us.ibm.com]

Student Modeling for Language Tutors
Workshop at AIED 2005
12th International Conference on Artificial Intelligence in Education
18-22 July
Amsterdam, The Netherlands
See http://www.research.ibm.com/people/a/alpert/AIED05/

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 behavior?

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 as:
- 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 2005

Chairs:
Sherman R. Alpert
salpert@us.ibm.com
IBM T.J. Watson Research Center
Phone: +1 914 945 1874

Joseph E. Beck
joseph.beck@cmu.edu
Center for Automated Learning and Discovery
Carnegie Mellon University
Phone:  +1 412 268 5726

Program Committee:
Jack Mostow
Director, Project LISTEN,
Carnegie Mellon University
mostow@cs.cmu.edu

W. Lewis Johnson
Director, Center for Advanced Research in Technology for Education,
USC / Information Sciences Institute
johnson@ISI.EDU

Stephen A. LaRocca, Ph.D
Army Research Laboratory (ARTI)
slarocca@arl.army.mil

Lisa N. Michaud
Department of Mathematics and Computer Science,
Wheaton College
lmichaud@wheatoncollege.edu

Peter Fairweather
IBM T.J. Watson Research Center
pfairwea@us.ibm.com

Received on Wednesday, 23 March 2005 15:08:45 UTC