Mini-VLAT: A Short and Effective Measure of Visualization Literacy
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Abstract (arXiv)
The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time-effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini-VLAT, a brief but practical visualization literacy test. The Mini-VLAT is a 12-item short form of the 53-item Visualization Literacy Assessment Test (VLAT). The Mini-VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini-VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini-VLAT by demonstrating a strong positive correlation between study participants' Mini-VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini-VLAT items showed a similar pattern of validity and reliability as the 53-item VLAT. The results show that Mini-VLAT is a psychometrically sound and practical short measure of visualization literacy.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2023.
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Citing papers per year: 2024: 2; 2025: 6; 2026: 8.
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- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuningcited by 27
- 2025Benchmarking Visual Language Models on Standardized Visualization Literacy Testscited by 20
- 2024Visualization Literacy of Multimodal Large Language Models: A Comparative Studycited by 9
- 2025Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Uglycited by 9
- 2025See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMscited by 7
- 2025MapIQ: Evaluating Multimodal Large Language Models for Map Question Answeringcited by 5
- 2026Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Taskscited by 2
- 2025Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attentioncited by 1
- 2025DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessmentcited by 1
- 2026Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systemscited by 1
- 2026AnnoBench: A Benchmark for Visualization Annotation Generationcited by 1
- 2026Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reductioncited by 0
4 more
- 2026Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacycited by 0
- 2026Benchmarking Multimodal Large Language Models for Scientific Visualization Literacycited by 0
- 2026Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMscited by 0
- 2026LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventionscited by 0
- 2026LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventions21 Sep 2026
- 2026AnnoBench: A Benchmark for Visualization Annotation Generation28 Jul 2026
- 2026Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs17 Jul 2026
- 2026Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy16 Jul 2026
- 2026Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy2 Jun 2026
- 2026Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks20 May 2026
- 2026Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction5 Feb 2026
- 2026Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systems18 Jan 2026
- 2025DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment6 Aug 2025
- 2025Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention22 Jul 2025
- 2025MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering15 Jul 2025
- 2025See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs14 Apr 2025
4 more
- 2025Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly7 Apr 2025
- 2025Benchmarking Visual Language Models on Standardized Visualization Literacy Tests20 Mar 2025
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning29 Jul 2024
- 2024Visualization Literacy of Multimodal Large Language Models: A Comparative Study24 Jun 2024
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuningcited by 27
- 2025Benchmarking Visual Language Models on Standardized Visualization Literacy Testscited by 20
- 2024Visualization Literacy of Multimodal Large Language Models: A Comparative Studycited by 9
- 2025Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Uglycited by 9
- 2025See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMscited by 7
- 2025MapIQ: Evaluating Multimodal Large Language Models for Map Question Answeringcited by 5
- 2026Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systemscited by 1
- 2026Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacycited by 0
- 2026Benchmarking Multimodal Large Language Models for Scientific Visualization Literacycited by 0
- 2026Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMscited by 0
- 2026Attention-Guided Saliency Maps for Interpreting Visualization Literacy in VLMs17 Jul 2026
- 2026Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy16 Jul 2026
- 2026Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy2 Jun 2026
- 2026Do MLLMs See What We See? Analyzing Visualization Literacy Barriers in AI Systems18 Jan 2026
- 2025MapIQ: Evaluating Multimodal Large Language Models for Map Question Answering15 Jul 2025
- 2025See or Recall: A Sanity Check for the Role of Vision in Solving Visualization Question Answer Tasks with Multimodal LLMs14 Apr 2025
- 2025Probing the Visualization Literacy of Vision Language Models: the Good, the Bad, and the Ugly7 Apr 2025
- 2025Benchmarking Visual Language Models on Standardized Visualization Literacy Tests20 Mar 2025
- 2024Advancing Multimodal Large Language Models in Chart Question Answering with Visualization-Referenced Instruction Tuning29 Jul 2024
- 2024Visualization Literacy of Multimodal Large Language Models: A Comparative Study24 Jun 2024
- 2026Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Taskscited by 2
- 2025Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attentioncited by 1
- 2025DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessmentcited by 1
- 2026AnnoBench: A Benchmark for Visualization Annotation Generationcited by 1
- 2026Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reductioncited by 0
- 2026LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventionscited by 0
- 2026LegendBench: A Diagnostic Benchmark for Legend Understanding with Counterfactual Interventions21 Sep 2026
- 2026AnnoBench: A Benchmark for Visualization Annotation Generation28 Jul 2026
- 2026Toward AI VIS Co-Scientists: A General and End-to-End Agent Harness for Solving Complex Data Visualization Tasks20 May 2026
- 2026Exploring AI-Augmented Sensemaking of Patient-Generated Health Data: A Mixed-Method Study with Healthcare Professionals in Cardiac Risk Reduction5 Feb 2026
- 2025DRIVE-T: A Methodology for Discriminative and Representative Data Viz Item Selection for Literacy Construct and Assessment6 Aug 2025
- 2025Tell Me Without Telling Me: Two-Way Prediction of Visualization Literacy and Visual Attention22 Jul 2025