Literature DB >> 27875171

VLAT: Development of a Visualization Literacy Assessment Test.

Sukwon Lee, Sung-Hee Kim, Bum Chul Kwon.   

Abstract

The Information Visualization community has begun to pay attention to visualization literacy; however, researchers still lack instruments for measuring the visualization literacy of users. In order to address this gap, we systematically developed a visualization literacy assessment test (VLAT), especially for non-expert users in data visualization, by following the established procedure of test development in Psychological and Educational Measurement: (1) Test Blueprint Construction, (2) Test Item Generation, (3) Content Validity Evaluation, (4) Test Tryout and Item Analysis, (5) Test Item Selection, and (6) Reliability Evaluation. The VLAT consists of 12 data visualizations and 53 multiple-choice test items that cover eight data visualization tasks. The test items in the VLAT were evaluated with respect to their essentialness by five domain experts in Information Visualization and Visual Analytics (average content validity ratio = 0.66). The VLAT was also tried out on a sample of 191 test takers and showed high reliability (reliability coefficient omega = 0.76). In addition, we demonstrated the relationship between users' visualization literacy and aptitude for learning an unfamiliar visualization and showed that they had a fairly high positive relationship (correlation coefficient = 0.64). Finally, we discuss evidence for the validity of the VLAT and potential research areas that are related to the instrument.

Year:  2017        PMID: 27875171     DOI: 10.1109/TVCG.2016.2598920

Source DB:  PubMed          Journal:  IEEE Trans Vis Comput Graph        ISSN: 1077-2626            Impact factor:   4.579


  3 in total

1.  Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments.

Authors:  Katy Börner; Andreas Bueckle; Michael Ginda
Journal:  Proc Natl Acad Sci U S A       Date:  2019-02-05       Impact factor: 11.205

2.  A Meta-Model Integration for Supporting Knowledge Discovery in Specific Domains: A Case Study in Healthcare.

Authors:  Andrea Vázquez-Ingelmo; Alicia García-Holgado; Francisco José García-Peñalvo; Roberto Therón
Journal:  Sensors (Basel)       Date:  2020-07-22       Impact factor: 3.576

3.  Impacts of Visualizations on Decoy Effects.

Authors:  Yuin Jeong; Sangheon Oh; Younah Kang; Sung-Hee Kim
Journal:  Int J Environ Res Public Health       Date:  2021-12-01       Impact factor: 3.390

  3 in total

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