ExcelViZ: Automated Generation of High-Level, Adaptable Scatterplot Descriptions Based on a User Study

Publikation: Beitrag zu KonferenzenPaperBeigetragenBegutachtung

Beitragende

Abstract

Digital charts enable quick and effective analysis of complex data, while they pose a barrier for people with visual impairments. Image descriptions are intended to provide equivalent access to the chart’s content, which is challenging and requires interpretations of the data, especially for complex data sets. Therefore, this paper investigates which key messages and categories sighted students perceive in scatterplots based on two user studies. First, we discussed five exemplary scatterplots with a group of sixteen students, who are familiar with digital accessibility and scatterplots in the academic context. We further analyzed this sample of scatterplots through an online survey with 222 participants, primarily students. We also compared the statistical correlations with the correlations reported by the participants, finding a high degree of agreement on outliers and trends. The results highlight the potential of image descriptions for accessible data analysis and form the basis for their automated generation.

Details

OriginalspracheEnglisch
Seiten393–412
Seitenumfang20
PublikationsstatusVeröffentlicht - 1 Juni 2024
Peer-Review-StatusJa

Konferenz

Titel26th International Conference on Human-Computer Interaction
KurztitelHCI International 2024
Veranstaltungsnummer26
Dauer29 Juni - 4 Juli 2024
Webseite
BekanntheitsgradInternationale Veranstaltung
OrtWashington Hilton Hotel & online
StadtWashington DC
LandUSA/Vereinigte Staaten

Externe IDs

Scopus 85195884897

Schlagworte

Schlagwörter

  • Accessible Charts, Image Descriptions, Scatterplots, User Study