As mobile devices have become more common, the need for efficient methods of mobile device text entry has grown. With this growth comes new challenges, as the constraints imposed by the size, processing power, and design of mobile devices impairs traditional text entry mechanisms in ways not seen in previous text entry tasks. To combat this, researchers have developed a variety of text entry aids, such as automatic word completion and correction, that help the user input the desired text more quickly and accurately than unaided input. Text entry aids are able to produce meaningful gains by attempting to model user behavior. These aids rely on models of the language the user speaks and types in and of user typing behavior to understand the intent of a user's input. Because these models require a large body of supervised training data to build, they are often built offline using aggregate data from many users. When they wish to predict the behavior of a new user, they do so by comparing their input to the behavior of the ``average'' user used to build the models.Alternatively, a model that is built on the current user's data rather than that of an average user may be better able to adapt to their individual quirks and provide better overall performance. However, to enable this personalized experience for a previously unseen user the system must be able to collect the data to build the models online, from the natural input provided by the user. This not only allows the system to better model the user's behavior, but it also allows it to continuously adapt to behavioral changes. This work examines this personalization and adaptation problem, with a particular focus on solving the online data collection problem.This work looks at the online data collection, personalization, and adaptation problems at two levels. In the first, it examines lower level text entry aids that attempt to help users input each individual character. Online data collection and personalization are examined in the context of one commonly deployed character-level text entry aid, key-target resizing. Several simple and computationally inexpensive data collection and assessment methods are proposed and evaluated. The results of these experiments suggest that by using these data assessment techniques we are able to dynamically build personalized models that outperform general models by observing less than one week's worth of text input from the average user. Additional analyses suggest that further improvements can be obtained by hybrid approaches that consider both aggregate and personalized data.We then step back and examine the data assessment and collection process for higher-level text entry aids. To do so we examine two text entry aids that work at the word level, automatic word correction and automatic word completion. Although their stated goal differs, these aids work similarly and, critically, fail similarly. To improve performance, data assessment methods that can detect cases of system failure are proposed. By automatically and dynamically detecting when a system fails for a given user, we are better able to understand user behavior and help the system overcome its shortfalls. The results of these experiments suggest that a careful examination of user dialogue behavior will allow the system to assess its own performance. Several methods for utilizing the self-assessment data for personalization are proposed and are shown to be plausibly able to improve performance.
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