Investigating the Tradeoffs of Everyday Text-Entry Collection Methods

要旨

Typing on mobile devices is a common and complex task. The act of typing itself thereby encodes rich information, such as the typing method, the context it is performed in, and individual traits of the person typing. Researchers are increasingly using a selection or combination of experience sampling and passive sensing methods in real-world settings to examine typing behaviours. However, there is limited understanding of the effects these methods have on measures of input speed, typing behaviours, compliance, perceived trust and privacy. In this paper, we investigate the tradeoffs of everyday data collection methods. We contribute empirical results from a four-week field study (N=26). Here, participants contributed by transcribing, composing, passively having sentences analyzed and reflecting on their contributions. We present a tradeoff analysis of these data collection methods, discuss their impact on text-entry applications, and contribute a flexible research platform for in the wild text-entry studies.

受賞
Best Paper
著者
André Rodrigues
Universidade de Lisboa, Lisboa, Portugal
Hugo Nicolau
Universidade de Lisboa, Lisbon, Portugal
André R.B.. Santos
University of Lisbon, Lisbon, Portugal
Diogo Branco
Universidade de Lisboa, Lisboa, Portugal
Jay Rainey
Newcastle University, Newcastle upon Tyne, United Kingdom
David Verweij
Newcastle University, Newcastle upon Tyne, United Kingdom
Jan David. Smeddinck
Newcastle University, Newcastle upon Tyne, United Kingdom
Kyle Montague
Northumbria University, Newcastle upon Tyne, Tyne and Wear, United Kingdom
Tiago Guerreiro
University of Lisbon, Lisbon, Portugal
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3501908

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: User Modeling

288-289
5 件の発表
2022-05-04 01:15:00
2022-05-04 02:30:00