Improving Discoverability and Expert Performance in Force-Sensitive Text Selection for Touch Devices with Mode Gauges

Abstract

Text selection on touch devices can be a difficult task for users. Letters and words are often too small to select directly, and the enhanced interaction techniques provided by the OS-magnifiers, selection handles, and methods for selecting at the character, word, or sentence level-often lead to as many usability problems as they solve. The introduction of force-sensitive touchscreens has added another enhancement to text selection (using force for different selection modes); however, these modes are difficult to discover and many users continue to struggle with accurate selection. In this paper we report on an investigation of the design of touch-based and force-based text selection mechanisms, and describe two novel text-selection techniques that provide improved discoverability, enhanced visual feedback, and a higher performance ceiling for experienced users. Two evaluations show that one design successfully combined support for novices and experts, was never worse than the standard iOS technique, and was preferred by participants.

Towards a Unified and Efficient Command Selection Mechanism for Touch-Based Devices Using Soft Keyboard Hotkeys

“…This metric measures discovery of SoftCuts when users are not required to use it to complete a task. Similar to [4,27], it is computed as the ratio between the number of participants who used SoftCuts in Mission1 and the number of participants who actually started Mission1 (Eq. 1).…”

Press-n-Paste: Copy-and-Paste Operations with Pressure-sensitive Caret Navigation for Miniaturized Surface in Mobile Augmented Reality

“…Caret movement is difficult with touch input because of finger occlusion [47] and the imprecision of interacting with a small display using fingers [21]. One of the solutions is granularity management [10,32] that alters the selection of textual units (e.g. character, word, sentence).…”

“…As a result, the alternation from character-level to word-level improves the selection speed and alleviates the user workloads. A most recent work proposes a text gauge [10] for text selection, namely ForceSelect, which achieves a full coverage of textual granularity in terms of characters, words, sentences, paragraphs and entire texts. Users with the text gauge employ 3D touch on an iPhone to select the suitable text granularity.…”

“…Thus, five options in a menu demonstrate a reasonable usability. Additionally, a pop-up pie menu is employed in a similar approach for direct manipulation on touchscreen [10]. However, we employ a linear menu at the upper edge to reserve the central area of the limited screen real estate on AR smartglasses [28].…”

“…All participants completed eight sessions for each solution, and a total of 725,560 touchpoints for GS and TE were tracked. A 15-minute warm-up session [10] was allowed before the sessions. At the beginning of each session, all participants were told in each session to complete the task as fast as possible.…”