writing

Efficiently entering text on interactive surfaces, such as touch-based tabletops, is an important concern. One novel solution is shape writing - the user strokes through all the letters in the word on a virtual keyboard without lifting his or her finger. While this technique can be used with any keyboard layout, the layout does impact the expected performance. In this paper, I investigate the influence of keyboard layout on expert text-entry performance for stroke-based text entry. Based on empirical data, I create a model of stroking through a series of points based on Fitts's law. I then use that model to evaluate various keyboard layouts for both tapping and stroking input. While the stroke-based technique seems promising by itself (i.e., there is a predicted gain of 17.3% for a Qwerty layout), significant additional gains can be made by using a more-suitable keyboard layout (e.g., the OPTI II layout is predicted to be 29.5% faster than Qwerty).

Collocation preferences represent the commonly used expressions, idioms, and word pairings of a language. Because collocation preferences arise from consensus usage, rather than a set of well-defined rules, they must be learned on a case-by-case basis, making them particularly challenging for non-native speakers of a language. To assist non-native speakers with these parts of a language, we developed AwkChecker, the first end-user tool geared toward helping non-native speakers detect and correct collocation errors in their writing. As a user writes, AwkChecker automatically flags collocation errors and suggests replacement expressions that correspond more closely to consensus usage. These suggestions include example usage to help users choose the best candidate. We describe AwkChecker's interface, its novel methods for detecting collocation errors and suggesting alternatives, and an early study of its use by non-native English speakers at our institution. Collectively, these contributions advance the state of the art in writing aids for non-native speakers.