Mobile text entry methods are typically evaluated by having study participants copy phrases. However, currently there is no available phrase set that has been composed by mobile users. Instead researchers have resorted to using invented phrases that probably suffer from low external validity. Further, there is no available phrase set whose phrases have been verified to be memorable. In this paper we present a collection of mobile email sentences written by actual users on actual mobile devices. We obtained our sentences from emails written by Enron employees on their BlackBerry mobile devices. We provide empirical data on how easy the sentences were to remember and how quickly and accurately users could type these sentences on a full-sized keyboard. Using this empirical data, we construct a series of phrase sets we suggest for use in text entry evaluations.
A common methodology for evaluating text entry methods is to ask participants to transcribe a predefined set of memorable sentences or phrases. In this article, we explore if we can complement the conventional transcription task with a more externally valid composition task. In a series of large-scale crowdsourced experiments, we found that participants could consistently and rapidly invent high quality and creative compositions with only modest reductions in entry rates. Based on our series of experiments, we provide a best-practice procedure for using composition tasks in text entry evaluations. This includes a judging protocol which can be performed either by the experimenters or by crowdsourced workers on a microtask market. We evaluated our composition task procedure using a text entry method unfamiliar to participants. Our empirical results show that the composition task can serve as a valid complementary text entry evaluation method.
We propose a new research direction for eye-typing which is potentially much faster: dwell-free eye-typing. Dwell-free eye-typing is in principle possible because we can exploit the high redundancy of natural languages to allow users to simply look at or near their desired letters without stopping to dwell on each letter. As a first step we created a system that simulated a perfect recognizer for dwellfree eye-typing. We used this system to investigate how fast users can potentially write using a dwell-free eye-typing interface. We found that after 40 minutes of practice, users reached a mean entry rate of 46 wpm. This indicates that dwell-free eye-typing may be more than twice as fast as the current state-of-the-art methods for writing by gaze. A human performance model further demonstrates that it is highly unlikely traditional eye-typing systems will ever surpass our dwell-free eye-typing performance estimate.
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