[ACT-R-users] Thesis Defense: Leonghwee Teo, Monday, February 21, 3:30pm @ GHC 6115, CMU

[Leonghwee Teo]

THESIS DEFENSE

Modeling Goal-Directed User Exploration in Human-Computer Interaction

Leonghwee Teo, Human-Computer Interaction Institute (HCII), Carnegie
Mellon University
Monday, February 21, 2011
3:30pm, Gates and Hillman Centers 6115



COMMITTEE

Bonnie E. John, HCII (Chair)
Aniket Kittur, HCII
Brad A. Myers, HCII
Peter L. Pirolli, PARC



ABSTRACT

Designing user-interfaces so that first-time or infrequent users can
accomplish their goals by exploration has been an enduring challenge
in Human-Computer Interaction (HCI). Iterative user-testing is an
effective but costly method to develop user-interfaces that support
use through exploration. A complementary method is to use modeling
tools that can generate predictions of user exploration given a
user-interface and a goal description.

Recent computational models of goal-directed user exploration have
focused on predicting user exploration of websites and demonstrated
how predictions can inform user-interface design. These models employ
the common concepts of label following and information scent: that the
user's choice is partly determined by the semantic relevance between
the user's goal and the options presented in the user-interface.
However, in addition to information scent, other factors including the
layout position and grouping of options in the user-interface also
affect user exploration and the likelihood of success.

This dissertation contributes a new model of goal-directed user
exploration, called CogTool-Explorer, which considers the layout
position and the grouping of options in the user-interface in concert
with a serial evaluation visual search process and information scent.
Tests show that predictions from CogTool-Explorer match participant
data better than alternative models that do not consider layout
position and grouping. This dissertation work has also integrated the
CogTool-Explorer model into an existing modeling tool, called CogTool,
making it easier for other researchers and practitioners to setup and
generate predictions of likely user exploration paths and task
performance using CogTool-Explorer.