# DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment

arXiv: https://arxiv.org/abs/2508.04160 · HTML: https://mlsanity.com/paper/2508.04160/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 42 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Mini-VLAT: A Short and Effective Measure of Visualization Literacy](https://mlsanity.com/paper/2304.07905/) (2023)
- [Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations](https://mlsanity.com/paper/2112.12364/) (2021)
- [How to evaluate data visualizations across different levels of understanding](https://mlsanity.com/paper/2009.01747/) (2020)
- [The Risks of Ranking: Revisiting Graphical Perception to Model Individual Differences in Visualization Performance](https://mlsanity.com/paper/2212.10533/) (2022)

## Built on by (most cited first, 1 of 1)

- [Context-Mediated Domain Adaptation in Multi-Agent Sensemaking Systems](https://mlsanity.com/paper/2603.24858/) (2026)

## Abstract (arXiv)

The underspecification of progressive levels of difficulty in measurement constructs design and assessment tests for data visualization literacy may hinder the expressivity of measurements in both test design and test reuse. To mitigate this problem, this paper proposes DRIVE-T (Discriminating and Representative Items for Validating Expressive Tests), a methodology designed to drive the construction and evaluation of assessment items. Given a data vizualization, DRIVE-T supports the identification of task-based items discriminability and representativeness for measuring levels of data visualization literacy. DRIVE-T consists of three steps: (1) tagging task-based items associated with a set of data vizualizations; (2) rating them by independent raters for their difficulty; (3) analysing raters' raw scores through a Many-Facet Rasch Measurement model. In this way, we can observe the emergence of difficulty levels of the measurement construct, derived from the discriminability and representativeness of task-based items for each data vizualization, ordered into Many-Facets construct levels. In this study, we show and apply each step of the methodology to an item bank, which models the difficulty levels of a measurement construct approximating a latent construct for data visualization literacy. This measurement construct is drawn from semiotics, i.e., based on the syntax, semantics and pragmatics knowledge that each data visualization may require to be mastered by people. The DRIVE-T methodology operationalises an inductive approach, observable in a post-design phase of the items preparation, for formative-style and practice-based measurement construct emergence. A pilot study with items selected through the application of DRIVE-T is also presented to test our approach.