Session:「Innovative Text Entry Systems」

Investigating Tilt-based Gesture Keyboard Entry for Single-Handed Text Entry on Large Devices

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025520

論文アブストラクト: The popularity of mobile devices with large screens is making single-handed interaction difficult. We propose and evaluate a novel design point around a tilt-based text entry technique which supports single handed usage. Our technique is based on the gesture keyboard (shape writing). However, instead of drawing gestures with a finger or stylus, users articulate a gesture by tilting the device. This can be especially useful when the user's other hand is otherwise encumbered or unavailable. We show that novice users achieve an entry rate of 15 words-per-minute (wpm) after minimal practice. A pilot longitudinal study reveals that a single participant achieved an entry rate of 32 wpm after approximate 90 minutes of practice. Our data indicate that tilt-based gesture keyboard entry enables walk-up use and provides a suitable text entry rate for occasional use and can act as a promising alternative to single-handed typing in certain situations.

日本語のまとめ:

単語をなぞって入力する Shape Writing を片手チルト(傾き)操作で行った研究.傾きを使うので VR など手元が見えない場合にも活かせるとしており,実験結果から慣れてくれば操作速度は上がると示唆された.

Modelling Learning of New Keyboard Layouts

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025580

論文アブストラクト: Predicting how users learn new or changed interfaces is a long-standing objective in HCI research. This paper contributes to understanding of visual search and learning in text entry. With a goal of explaining variance in novices' typing performance that is attributable to visual search, a model was designed to predict how users learn to locate keys on a keyboard: initially relying on visual short-term memory but then transitioning to recall-based search. This allows predicting search times and visual search patterns for completely and partially new layouts. The model complements models of motor performance and learning in text entry by predicting change in visual search patterns over time. Practitioners can use it for estimating how long it takes to reach the desired level of performance with a given layout.

日本語のまとめ:

新しいキーボード配列の学習予測モデルを考案.長期・短期記憶,慣れた配列と新しい配列の記憶を取捨選択する重み付けなどを文献から引用しつつモデル構成.モデル曰く,例えば Dvorak 配列は6時間学習でそれなりの速度になる.

Word Clarity as a Metric in Sampling Keyboard Test Sets

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025701

論文アブストラクト: Test sets play an essential role in evaluating text entry techniques. In this paper, we argue that in addition to the widely adopted metric of bigram representativeness and memorability, word clarity should also be considered as a metric when creating test sets from the target dataset. Word clarity quantifies the extent to which a word is likely to confuse with other words on a keyboard. We formally define word clarity, derive equations calculating it, and both theoretically and empirically show that word clarity has a significant effect on text entry performance: it can yield up to 26.4% difference in error rate, and 25% difference in input speed. We later propose a Pareto optimization method for sampling test sets with different sizes, which optimizes the word clarity and bigram representativeness, and memorability of the test set. The obtained test sets are published on the Internet.

日本語のまとめ:

キーボード上での単語距離を考案し,単語距離とタイプエラー率の相関を検証.単語距離から明瞭度を定め,実験に適したテスト用の単語データセットをパレート最適解で導出した.実際にデータセットは公開されている.

Quantifying Aversion to Costly Typing Errors in Expert Mobile Text Entry

論文URL: http://dl.acm.org/citation.cfm?doid=3025453.3025695

論文アブストラクト: Text entry is an increasingly important activity for mobile device users. As a result, increasing text entry speed of expert typists is an important design goal for physical and soft keyboards. Mathematical models that predict text entry speed can help with keyboard design and optimization. Making typing errors when entering text is inevitable. However, current models do not consider how typists themselves reduce the risk of making typing errors (and lower error frequency) by typing more slowly. We demonstrate that users respond to costly typing errors by reducing their typing speed to minimize typing errors. We present a model that estimates the effects of risk aversion to errors on typing speed. We estimate the magnitude of this speed change, and show that disregarding the adjustments to typing speed that expert typists use to reduce typing errors leads to overly optimistic estimates of maximum errorless expert typing speeds.

日本語のまとめ:

キーボードのタイプエラーに対するユーザのリスク回避行動を含めた,タイピング時間のモデル化.リスク回避による遅延を考慮すると,実際の最速値に最適であり,エラー修正時間まで考慮すると,実際の平均値により近づいた.