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Characterizing Data Visualization Literacy: a Systematic Literature Review

Sara Beschi, Davide Falessi, Silvia Golia, Angela Locoro

TL;DR

This paper tackles the lack of a unified understanding and measurement of Data Visualization Literacy (DVL). It uses PRISMA-guided systematic review to synthesize 43 reports (2014–2024) on DVL definitions, models, and assessments, distinguishing basic vs applied research. Key contributions include cataloging terminologies, outlining construct models, and evaluating validation practices, while revealing substantial fragmentation and a need for a standard DVL construct. The findings highlight the importance of user-centered, context-specific assessments and point to future work on broader data-viz coverage, robust validation, and educational integration. The work has practical impact on designers, educators, and policymakers aiming to improve data-driven decision making.

Abstract

With the advent of the data era, and of new, more intelligent interfaces for supporting decision making, there is a growing need to define, model and assess human ability and data visualizations usability for a better encoding and decoding of data patterns. Data Visualization Literacy (DVL) is the ability of encoding and decoding data into and from a visual language. Although this ability and its measurement are crucial for advancing human knowledge and decision capacity, they have seldom been investigated, let alone systematically. To address this gap, this paper presents a systematic literature review comprising 43 reports on DVL, analyzed using the PRISMA methodology. Our results include the identification of the purposes of DVL, its satellite aspects, the models proposed, and the assessments designed to evaluate the degree of DVL of people. Eventually, we devise many research directions including, among the most challenging, the definition of a (standard) unifying construct of DVL.

Characterizing Data Visualization Literacy: a Systematic Literature Review

TL;DR

This paper tackles the lack of a unified understanding and measurement of Data Visualization Literacy (DVL). It uses PRISMA-guided systematic review to synthesize 43 reports (2014–2024) on DVL definitions, models, and assessments, distinguishing basic vs applied research. Key contributions include cataloging terminologies, outlining construct models, and evaluating validation practices, while revealing substantial fragmentation and a need for a standard DVL construct. The findings highlight the importance of user-centered, context-specific assessments and point to future work on broader data-viz coverage, robust validation, and educational integration. The work has practical impact on designers, educators, and policymakers aiming to improve data-driven decision making.

Abstract

With the advent of the data era, and of new, more intelligent interfaces for supporting decision making, there is a growing need to define, model and assess human ability and data visualizations usability for a better encoding and decoding of data patterns. Data Visualization Literacy (DVL) is the ability of encoding and decoding data into and from a visual language. Although this ability and its measurement are crucial for advancing human knowledge and decision capacity, they have seldom been investigated, let alone systematically. To address this gap, this paper presents a systematic literature review comprising 43 reports on DVL, analyzed using the PRISMA methodology. Our results include the identification of the purposes of DVL, its satellite aspects, the models proposed, and the assessments designed to evaluate the degree of DVL of people. Eventually, we devise many research directions including, among the most challenging, the definition of a (standard) unifying construct of DVL.

Paper Structure

This paper contains 33 sections, 1 equation, 6 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. History and Fortune of the Term Visual Literacy
  4. The Usability and Accessibility of Data Viz
  5. The DVL Assessment
  6. Method
  7. Research Questions
  8. Search Process
  9. Papers Selection
  10. Data Extraction
  11. Results
  12. RQ1. Terms and Definitions for DVL
  13. RQ2. Basic Research Reports
  14. Defining, Promoting and Supporting DVL skills
  15. Constructs for assessing the DVL property
  16. ...and 18 more sections

Figures (6)

  • Figure 1: PRISMA Flow Diagram followed in this review.
  • Figure 2: The distribution of the 43 reports considered in this systematic review by journal title.
  • Figure 3: The distribution of the 43 reports considered in this systematic review by publication date.
  • Figure 4: Frequency distribution of data viz (yellow bars) and combination of data viz and tests (blue bars)
  • Figure 5: Distribution of the reports with respect to the number of items involved in the questionnaire
  • ...and 1 more figures