1993). Our NM design choices should also have an
M. Dzikovska, L. Galescu and M. Swift. 2006. Ches-
equivalent in a new domain (e.g. displaying the
ter: Towards a Personal Medication Advisor. Journal
recognized user answer can be the equivalent of
of Biomedical Informatics, 39(5).
the correct answers). Other NM usages can also be
J. Allen, G. Ferguson and A. Stent. 2001. An architec-
imagined: e.g. reducing the length of the system
ture for more realistic conversational systems. In
turns by removing text information that is implic-
Proc. of Intelligent User Interfaces.
itly represented in the NM.
J. Cassell, Y. I. Nakano, T. W. Bickmore, C. L. Sidner
and C. Rich. 2001. Non-Verbal Cues for Discourse
7 Conclusions & Future work
Structure. In Proc. of ACL.
In this paper we explore the utility of the Naviga-
A. Graesser, K. Moreno, J. Marineau, A. Adcock, A.
tion Map, a graphical representation of the dis-
Olney and N. Person. 2003. AutoTutor improves deep
course structure. As our first step towards under-
learning of computer literacy: Is it the dialog or the
standing the benefits of the NM, we ran a user
talking head? In Proc. of Artificial Intelligence in
Education (AIED).
study to investigate if users perceive the NM as
useful. From the users’ perspective, the NM pres-
B. Grosz and C. L. Sidner. 1986. Attentions, intentions
ence allows them to better identify and follow the
and the structure of discourse. Computational Lin-
guistics, 12(3).
tutoring plan and to better integrate the instruction.
It was also easier for users to concentrate and to
D. Higgins, J. Burstein, D. Marcu and C. Gentile. 2004.
learn from the system if the NM was present. Our
Evaluating Multiple Aspects of Coherence in Student
Essays. In Proc. of HLT-NAACL.
preliminary analysis on objective metrics shows
that users’ preference for the NM version is re-
J. Hirschberg and C. Nakatani. 1996. A prosodic analy-
flected in more correct user answers and less
sis of discourse segments in direction-giving mono-
logues. In Proc. of ACL.
speech recognition problems in the NM version.
E. Hovy. 1993. Automated discourse generation using
These findings motivate future work in under-
discourse structure relations. Articial Intelligence,
standing the effects of the NM. We would like to
63(Special Issue on NLP).
continue our objective metrics analysis (e.g. see if
D. Litman and S. Silliman. 2004. ITSPOKE: An intelli-
users are better in the NM condition at updating
gent tutoring spoken dialogue system. In Proc. of
their essay and at answering questions that require
HLT/NAACL.
combining facts previously discussed). We also
S. Oviatt, R. Coulston and R. Lunsford. 2004. When Do
plan to run an additional user study with a be-
tween-subjects experimental design geared towards
We Interact Multimodally? Cognitive Load and Mul-
timodal Communication Patterns. In Proc. of Interna-
objective metrics. The experiment will have two
tional Conference on Multimodal Interfaces.
conditions: NM present/absent for all problems.
R. Passonneau and D. Litman. 1993. Intention-based
The conditions will then be compared in terms of
segmentation: Human reliability and correlation with
various objective metrics. We would also like to
linguistic cues. In Proc. of ACL.
know which information sources represented in the
H. Pon-Barry, K. Schultz, E. O. Bratt, B. Clark and S.
NM (e.g. discourse segment purpose, limited hori-
Peters. 2006. Responding to Student Uncertainty in
zon, correct answers) has the biggest impact.
Spoken Tutorial Dialogue Systems. International
Journal of Artificial Intelligence in Education, 16.
Acknowledgements
C. Rich and C. L. Sidner. 1998. COLLAGEN: A Col-
This work is supported by NSF Grants 0328431
laboration Manager for Software Interface Agents.
and 0428472. We would like to thank Shimei Pan,
User Modeling and User-Adapted Interaction, 8(3-4).
Pamela Jordan and the ITSPOKE group.
M. Rotaru and D. Litman. 2006. Exploiting Discourse
Structure for Spoken Dialogue Performance Analy-
sis. In Proc. of EMNLP.
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