Award Abstract # 1713567
Data Visualization Literacy: Research and Tools that Advance Public Understanding of Scientific Data

NSF Org: DRL
Division of Research on Learning in Formal and Informal Settings (DRL)
Recipient: TRUSTEES OF INDIANA UNIVERSITY
Initial Amendment Date: June 13, 2017
Latest Amendment Date: November 18, 2022
Award Number: 1713567
Award Instrument: Standard Grant
Program Manager: Arlene de Strulle
adestrul@nsf.gov
 (703)292-5117
DRL
 Division of Research on Learning in Formal and Informal Settings (DRL)
EDU
 Directorate for STEM Education
Start Date: August 1, 2017
End Date: July 31, 2023 (Estimated)
Total Intended Award Amount: $1,355,236.00
Total Awarded Amount to Date: $1,355,236.00
Funds Obligated to Date: FY 2017 = $1,355,236.00
History of Investigator:
  • Katy Borner (Principal Investigator)
    katy@indiana.edu
  • Joe Heimlich (Co-Principal Investigator)
  • Stephen Uzzo (Co-Principal Investigator)
  • Kylie Peppler (Co-Principal Investigator)
  • Bryan Kennedy (Co-Principal Investigator)
Recipient Sponsored Research Office: Indiana University
107 S INDIANA AVE
BLOOMINGTON
IN  US  47405-7000
(317)278-3473
Sponsor Congressional District: 09
Primary Place of Performance: Indiana University
IN  US  47401-3654
Primary Place of Performance
Congressional District:
09
Unique Entity Identifier (UEI): YH86RTW2YVJ4
Parent UEI:
NSF Program(s): AISL
Primary Program Source: 04001718DB NSF Education & Human Resource
Program Reference Code(s):
Program Element Code(s): 725900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.076

ABSTRACT

As the world is increasingly dependent upon computing and computational processes associated with data analysis, it is essential to gain a better understanding of the visualization technologies that are used to make meaning of massive scientific data. It is also essential that the infrastructure, the very means by which technologies are developed for improving the public's engagement in science itself, be better understood. Thus, this AISL Innovations in Development project will address the critical need for the public to learn how to interpret and understand highly complex and visualized scientific data. The project will design, develop and study a new technology platform, xMacroscope, as a learning tool that will allow visitors at the Science Museum of Minnesota and the Center of Science and Industry, to create, view, understand, and interact with different data sets using diverse visualization types. The xMacroscope will support rapid research prototyping of public experiences at selected exhibits, such as collecting data on a runner's speed and height and the visualized representation of such data. The xMacroscope will provide research opportunities for exhibit designers, education researchers, and learning scientists to study diverse audiences at science centers in order to understand how learning about data through the xMacroscope tool may inform definitions of data literacy. The research will advance the state of the art in visualization technology, which will have broad implications for teaching and learning of scientific data in both informal and formal learning environments. The project will lead to better understanding by science centers on how to present data to the public more effectively through visualizations that are based upon massive amounts of data. Technology results and research findings will be disseminated broadly through professional publications and presentations at science, education, and technology conferences. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants.

The project is driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The research will seek to define, measure, and advance data visualization literacy. The project will engage the public in using the xMacrocope at the Science Museum of Minnesota and at the Center of Science and Industry's (COSI) science museum and research center in Columbus, Ohio. In both museum settings the public will interact with different datasets and diverse types of visualizations. Using the xMacroscope platform, personal attributes and capabilities will be measured and personalized data visualizations will be constructed. Existing theories of learning (constructivist and constructionist) will be extended to capture the learning and use of data visualization literacy. In addition, the project team will conduct a meta-review related to different types of literacy and will produce a definition with performance measures to assess data visualization literacy - currently broadly defined in the project as the ability to read, understand, and create data visualizations. The research has potential for significant impact in the field of science and technology education and education research on visual learning. It will further our understanding of the nature of data visualization literacy learning and define opportunities for visualizing data in ways that are both personally and culturally meaningful. The project expects to advance the understanding of the role of personalization in the learning process using iterative design-based research methodologies to advance both theory and practice in informal learning settings. An iterative design process will be applied for addressing the research questions by correlating visualizations to individual actions and contributions, exploring meaning-making studies of visualization construction, and testing the xMacroscope under various conditions of crowdedness and busyness in a museum context. The evaluation plan is based upon a logic model and the evaluation will iteratively inform the direction, process, and productivity of the project.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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(Showing: 1 - 10 of 11)
Börner, Katy and Scrivner, Olga and Gallant, Mike and Ma, Shutian and Liu, Xiaozhong and Chewning, Keith and Wu, Lingfei and Evans, James A. "Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy" Proceedings of the National Academy of Sciences , v.115 , 2018 10.1073/pnas.1804247115 Citation Details
Börner, Katy and Ginda, Michael and Bueckle, Andreas "Data visualization literacy: Definitions, conceptual frameworks, exercises, and assessments" Proceedings of the National Academy of Sciences of the United States of America , v.116 , 2019 10.1073 Citation Details
Azoulay, Pierre and Graff-Zivin, Joshua and Uzzi, Brian and Wang, Dashun and Williams, Heidi and Evans, James A. and Jin, Ginger Zhe and Lu, Susan Feng and Jones, Benjamin F. and Börner, Katy and Lakhani, Karim R. and Boudreau, Kevin J. and Guinan, Eva C. "Toward a more scientific science" Science , v.361 , 2018 10.1126/science.aav2484 Citation Details
Scrivner, Olga and Nguyen, Thuy and Simon, Kosali and Middaugh, Esmé and Taska, Bledi and Börner, Katy and Cook, Benjamin "Job postings in the substance use disorder treatment related sector during the first five years of Medicaid expansion" PLOS ONE , v.15 , 2020 10.1371/journal.pone.0228394 Citation Details
Börner, Katy and Silva, Filipi Nascimento and Milojevi, Staa "Visualizing big science projects" Nature Reviews Physics , v.3 , 2021 https://doi.org/10.1038/s42254-021-00374-7 Citation Details
Börner, Katy and Scrivner, Olga and Cross, Leonard E. and Gallant, Michael and Ma, Shutian and Martin, Adam S. and Record, Lisel and Yang, Haici and Dilger, Jonathan M. "Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)" PLOS ONE , v.15 , 2020 https://doi.org/10.1371/journal.pone.0242984 Citation Details
Ploszaj, Adam and Yan, Xiaoran and Börner, Katy "The impact of air transport availability on research collaboration: A case study of four universities" PLOS ONE , v.15 , 2020 https://doi.org/10.1371/journal.pone.0238360 Citation Details
Peppler, Kylie and Keune, Anna and Han, Ariel "Cultivating data visualization literacy in museums" Information and Learning Sciences , v.122 , 2021 https://doi.org/10.1108/ILS-04-2020-0132 Citation Details
Ginda, Michael and Richey, Michael C. and Cousino, Mark and Börner, Katy and Dalby, Andrew R. "Visualizing learner engagement, performance, and trajectories to evaluate and optimize online course design" PLOS ONE , v.14 , 2019 10.1371/journal.pone.0215964 Citation Details
Galis, Zorina S. and Herr, Bruce W. and Daptardar, Santoshmurti S. and Sydykanova, Medina and Börner, Katy "Then and Now, Mapping the 25 Year Evolution and Impact of North American Vascular Biology Organization Science Through Publications of its Founding and Current Members" Frontiers in Research Metrics and Analytics , v.5 , 2020 https://doi.org/10.3389/frma.2020.591090 Citation Details
Börner, Katy and Simpson, Adam H. and Bueckle, Andreas and Goldstone, Robert L. "Science map metaphors: a comparison of network versus hexmap-based visualizations" Scientometrics , v.114 , 2018 10.1007/s11192-017-2596-3 Citation Details
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PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This work was driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The project focused on designing, developing, and studying a new technology platform, xMacroscope, as a learning tool that allows visitors at the Science Museum of Minnesota and the Center of Science and Industry to create, view, and interact with different data sets using diverse visualization types. The xMacroscope supports rapid research prototyping of public experiences at selected exhibits, where visitors could build visualizations based on data they generated during their exhibit experience.

Using the xMacroscope platform, personal attributes and capabilities were captured and used in the construction of personalized data visualizations. The researchers applied theories of learning?constructivist and constructionist?to better understand users? expressions of data visualization literacy. In addition, the project team conducted a meta-review related to different types of literacy and produced a definition with performance measures to assess data visualization literacy?broadly defined in the project as the ability to read, understand, and create data visualizations.

Findings from the research indicate that engagement with the xMacroscope supports the reading of data visualizations. Visitors were able to identify their very own data records as well as those of other group members. They actively engaged in producing different visualizations of 100s of data records by changing data visualization types and graphic variable types. The research also uncovered how users read data visualizations to identify data patterns and trends, particularly when exploring geomap visualization data. Additionally, having a relationship to the data entered (e.g., walking times between two points) supported the identification of measurement errors, data outliers located in  the graph, and considerations for producing a reliable data record and visualization. In the process, the research defines, facilitates, measures, and advances data visualization literacy.

This project has achieved the three following goals:

1. xMacroscope: A rapid-prototyping research and exhibit design platform for use on the museum floor was developed and published on GitHub. Cabinetry building and exhibit setup instructions are available at http://xmacroscope.org/research.html. The xMacroscope provides a way for visitors to build and interpret visualizations that use data generated by the visitors during their exhibit experience. The integration of data gathered via motion sensors, combined with an easy-to-use interface, guides visitors in building various types of data visualizations using sensor data. This provides a novel platform for conducting research on the efficacy of different methods for improving data visualization literacy.

2. The research team developed a definition for and performance measures to assess data visualization literacy by conducting a meta-review of definitions, standards, and assessment programs for different types of literacy (e.g., textual, visual, mathematical). It addressed the key issue of how people understand and relate to data in their lives, how they read data visualizations, and how understanding these visualizations affect their actions. In the process, the team extended the Visualization Design and Sense Making (VDSM) framework to guide the design and implementation of effective xMacroscope experiences and to measure the outcomes of interventions that engage visitors in reading, making, or explaining data visualizations.

3. The results of the study were disseminated broadly via scientific publications and presentations at key conferences relating to information science, cognitive science, learning science, and informal education. The xMacroscope platform, being open-source, makes it possible to rapidly deploy and research different visualization workflows and user interfaces, providing museum audiences with new and compelling interactive data visualization tools and experiences. Research results and development insights are written up in the"xMacroscope User Guide" for use by informal science practitioners. This guide helps translate research results and code development to tool designers and museum exhibit developers. The guide, code, and distilled action items on the development and utilization of the xMacroscope was published via Informalscience.org, GitHub, and a small and focused website (http://xmacroscope.org) that informal science designers and educators can use in their everyday work.


Last Modified: 10/17/2023
Modified by: Katy Borner

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