Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University
Modeling Goal-Directed User Exploration
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. 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 userÔs choice is partly determined by the semantic relevance between the userÔs 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.