| Summary: | Software applications continue to grow in terms of the number of features they offer,
making personalization increasingly important. Research has shown that most users
prefer the control afforded by an adaptable approach to personalization rather than
a system-controlled adaptive approach. Both types of approaches offer advantages
and disadvantages. No study, however, has compared the efficiency of the two
approaches.
In two controlled lab studies, we measured the efficiency of static, adaptive
and adaptable interfaces in the context of pull-down menus. These menu conditions
were implemented as a split menus, in which the top four items remained static,
were adaptable by the subject, or adapted according to the subject's frequently
and recently used items. The results of Study 1 showed that a static split menu
was significantly faster than an adaptive split menu. Also, when the adaptable
split menu was not the first condition presented to subjects, it was significantly
faster than the adaptive split menu, and not significantly different from the static
split menu. The majority of users preferred the adaptable menu overall. Several
implications for personalizing user interfaces based on these results are discussed.
One question which arose after Study 1 was whether prior exposure to the
menus and task has an effect on the efficiency of the adaptable menus. A second
study was designed to follow-up on the theory that prior exposure to different types
of menu layouts influences a user's willingness to customize. Though the observed
power of this study was low and no statistically significant effect of type of exposure
was found, a possible trend arose: that exposure to an adaptive interface may
have a positive impact on the user's willingness to customize. This and other secondary
results are discussed, along with several areas for future work. The research
presented in this thesis should be seen as an initial step towards a more thorough
comparison of adaptive and adaptable interfaces, and should provide motivation for
further development of adaptable interaction techniques. === Science, Faculty of === Computer Science, Department of === Graduate
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