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

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|February 6, 2019
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Summary
This summary is machine-generated.

A new Data Visualization Literacy Framework (DVL-FW) is introduced to comprehensively define, teach, and assess data visualization literacy (DVL). This framework supports both reading and constructing visualizations, essential skills in the information age.

Keywords:
assessmentdata visualizationinformation visualizationlearning sciencesliteracy

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Area of Science:

  • Information Science
  • Data Visualization
  • Educational Frameworks

Background:

  • The information age necessitates data visualization literacy (DVL) as a core skill, akin to textual and mathematical literacy.
  • Existing DVL definitions and frameworks are insufficient for effective teaching and assessment design.
  • There is a need for a comprehensive framework to guide DVL education.

Purpose of the Study:

  • To introduce a novel Data Visualization Literacy Framework (DVL-FW).
  • To provide a holistic approach for defining, teaching, and assessing DVL.
  • To bridge theoretical and procedural knowledge for DVL curriculum development.

Main Methods:

  • Development of a hierarchical typology of core DVL concepts.
  • Identification of process steps for extracting insights from data visualizations.
  • Integration of theoretical and procedural knowledge within the DVL-FW.

Main Results:

  • The DVL-FW promotes both the reading and construction of data visualizations.
  • The framework interlinks theoretical knowledge with practical procedural steps.
  • The DVL-FW provides a foundation for designing DVL curricula and assessment tools.

Conclusions:

  • The DVL-FW offers a comprehensive approach to data visualization literacy.
  • The framework's effectiveness has been informed by its use in teaching over 8,500 students.
  • The DVL-FW is a validated tool for advancing data visualization education and assessment.