Abstract

Visualization literacy assessments shape how we understand people's ability to interpret data, yet most existing instruments embed Western datasets and assumptions that limit their relevance for global audiences. This thesis argues that because data is personal, assessments must also be culturally grounded. We introduce a unified framework for adapting the Mini-VLAT into 22 regionally responsive short-form assessments, each retaining the structure of the original test while incorporating datasets and scenarios tailored to specific regions around the world. To demonstrate how such adaptations can be customized and validated, we present a detailed case study of a Ghana-adapted Mini-VLAT, developed in collaboration with local teachers and designers. The Ghana version preserves the cognitive skills of the original instrument while embedding locally meaningful data. Psychometric analysis shows that it is reliable (omega = 0.73) and correlates moderately with the original Mini-VLAT, providing initial evidence that culturally grounded instruments can maintain comparability. Altogether, this thesis offers both a global repository of adapted items and a validated country-specific example, providing a cohesive foundation for building inclusive, culturally relevant visualization literacy assessments.

Committee Chair

Alvitta Ottley

Committee Members

Ian Bogost, Caitlin Kelleher

Degree

Master of Science (MS)

Author's Department

Computer Science & Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

Fall 12-17-2025

Language

English (en)

Author's ORCID

https://orcid.org/0009-0000-8505-8014

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