Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Problem Solving: Dimensional Analysis01:08

Problem Solving: Dimensional Analysis

5.9K
Every mathematical equation that connects separate distinct physical quantities must be dimensionally consistent, which implies it must abide by two rules. For this reason, the concept of dimension is crucial. The first rule is that an equation's expressions on either side of an equality must have the exact same dimension, i.e., quantities of the same dimension can be added or removed. The second rule stipulates that all popular mathematical functions, such as exponential, logarithmic, and...
5.9K
Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.8K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.8K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.3K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.3K
Multiple Intelligences Theory01:20

Multiple Intelligences Theory

8.8K
Howard Gardner's theory of Multiple Intelligence proposes that there are nine distinct types of intelligence, each reflecting different ways of interacting with the world. Introduced in 1983 and expanded in subsequent years, Gardner's framework challenges the traditional notion of a single, generalized intelligence.
8.8K
Dimensional Analysis01:23

Dimensional Analysis

2.0K
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
2.0K
Dimensional Analysis03:40

Dimensional Analysis

59.2K
Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
59.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

PLUTO: A Public Value Assessment Tool.

IEEE computer graphics and applications·2026
Same author

Untangling Rhetoric, Pathos, and Aesthetics in Data Visualization.

IEEE transactions on visualization and computer graphics·2025
Same author

The Importance of Being Thorough: How Data Analysis Choices Impact the Perceived Relationship between Pollutants and Predictors.

Water research·2025
Same author

The AUTNES Online Panel Study 2017-2024: A Dataset of Austrian Voter Attitudes and Behavior.

Scientific data·2025
Same author

Visual Data Analysis of Time-Based Transport Optimizations.

IEEE computer graphics and applications·2025
Same author

Generations and political change.

West European politics·2025
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Jan 17, 2026

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.6K

A Multidimensional Assessment Method for Visualization Understanding (MdamV).

Antonia Saske, Laura Koesten, Torsten Moller

    IEEE Transactions on Visualization and Computer Graphics
    |January 14, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Assessing data visualization understanding requires more than just performance tests. A new Multidimensional Assessment Method for Visualization Understanding (MdamV) includes self-perception and critique, revealing deeper insights into how people interpret charts.

    More Related Videos

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    861
    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
    06:02

    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

    Published on: October 6, 2020

    2.6K

    Related Experiment Videos

    Last Updated: Jan 17, 2026

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.6K
    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment
    08:25

    Polar Histogram Visualization of Acute Stress Disorder Scale Scores for Comprehensive Clinical Assessment

    Published on: December 6, 2024

    861
    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios
    06:02

    Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

    Published on: October 6, 2020

    2.6K

    Area of Science:

    • Data Visualization
    • Learning Sciences
    • Human-Computer Interaction

    Background:

    • Current assessment methods for data visualization understanding primarily rely on performance tests, such as value retrieval tasks.
    • These methods often overlook crucial factors influencing comprehension, including numeracy, familiarity with graph types, and aesthetic perception.
    • Existing instruments inadequately capture the multifaceted nature of visualization understanding.

    Purpose of the Study:

    • To design and validate a comprehensive assessment tool, the Multidimensional Assessment Method for Visualization Understanding (MdamV).
    • To integrate task-based performance measures with self-perceived abilities and qualitative critique for a holistic evaluation.
    • To explore visualization understanding as a multifaceted process grounded in learning sciences.

    Main Methods:

    • Developed the Multidimensional Assessment Method for Visualization Understanding (MdamV), incorporating six dimensions: Comprehending, Decoding, Aestheticizing, Critiquing, Reading, and Contextualizing.
    • Administered MdamV to a representative survey sample (N=438) in Austria, utilizing climate data presented in line and bar charts.
    • Collected data on task performance, self-assessed numeracy, graph familiarity, and open-ended critiques.

    Main Results:

    • Validation data indicated that approximately 25% of respondents struggled with comprehending basic data units.
    • Around 20% of participants reported unfamiliarity with the presented chart types (line and bar charts).
    • Self-assessed numeracy demonstrated a significant positive correlation with data reading performance (p=0.0004).

    Conclusions:

    • The Multidimensional Assessment Method for Visualization Understanding (MdamV) offers a more complete evaluation of how individuals understand data visualizations.
    • Visualization understanding is a situated process, influenced by individual factors and the specific visualization being analyzed, extending beyond mere task performance.
    • Future assessments should incorporate multidimensional approaches to capture the full spectrum of visualization comprehension.