Name
Abstract | ||
|---|---|---|
We are often confronted with information interfaces designed in an unfamiliar language, especially in an increasingly globalized world, where the language barrier inhibits interaction with the system. In our work, we explore the design space for building interfaces that can detect the user's language proficiency. Specifically, we look at how a user's gaze properties can be used to detect whether the interface is presented in a language they understand. We report a study (N=21) where participants were presented with questions in multiple languages, whilst being recorded for gaze behavior. We identified fixation and blink durations to be effective indicators of the participants' language proficiencies. Based on these findings, we propose a classification scheme and technical guidelines for enabling language proficiency awareness on information displays using gaze data. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3025453.3025601 | CHI |
Keywords | Field | DocType |
Eye-tracking, machine learning, language-aware interfaces, adaptive interfaces | Language barrier,Design space,Language proficiency,Gaze,Computer science,Classification scheme,Eye tracking,Human–computer interaction,Multimedia | Conference |
Citations | PageRank | References |
4 | 0.72 | 36 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jakob Karolus | 1 | 10 | 6.61 |
| Pawel W. Wozniak | 2 | 127 | 35.17 |
| Lewis L. Chuang | 3 | 114 | 19.13 |
| Albrecht Schmidt | 4 | 6495 | 696.81 |