Information Technology Reference
In-Depth Information
In this example, we have focused on Task A, which is by nature a global
information foraging within the entire thematic space. Users switched to local search
for subsequent tasks. We have touched upon the shrinking-scope tendency in this
study, but studies of the full range of tasks with reference to Shneiderman's task-data
type taxonomy should lead to deeper insights to how users interact with visual-
spatial interfaces.
As far as the resultant HMMs are concerned, a clearer understanding and
interpretation of various characteristics manifested by paths selected by HMMs is
certainly desirable. We have only analyzed a small portion of the data generated
from our experiment. Among twelve combinations of visual-spatial interfaces and
underlying thematic spaces, we have only studied one pair - Alcohol in MST.
In addition to animations of trails and HMM-paths, one can use ghost avatars to
traverse the thematic space along with the real users. Ghost avatars can travel along
HMM-generated paths as well as actual trails, which will in turn inspire other users
and draw their attention to profitable areas in information foraging.
In conclusion, many of our expectations have been confirmed in the visualization
and animation of trails of information foragers in thematic spaces. The task we
have studied is global information foraging in nature. The initial integration of the
optimal information foraging and Hidden Markov Models is promising, especially
with the facilities to animate user trails within the thematic spaces.
Visualizing an information foraging process has led to valuable insights into how
users explore and navigate through thematic spaces. The only visible navigation
cues for users in these spaces are structures resulted from a spatial-semantic
mapping. Labeling in its own right is a challenging issue - how to generate the
most meaningful labels and summarize unstructured documents. Users have indeed
raised the issue concerning labeling local areas in the thematic space. However,
because the aim of this study was to investigate information foraging behavior, it
has been decided not to label document clusters for users in the experiment.
The combination of the optimal information foraging theory and Hidden Markov
models plays an essential part in the study of users' navigation strategies. In future
studies, there are several possible routes to pursue. One can repeat the study with a
larger sample size of users and classify users according to their cognitive abilities or
other criteria. Then one can compare HMMs across different user classes and make
connections between information foraging behavior of users and their individual
differences. Future studies should expand the scope of tasks to cover a fuller
range of information foraging activities. Visual-spatial interfaces should be carefully
designed for future studies so that fundamental issues can be addressed.
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