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'Seeing' Data Like an Expert: An Eye-Tracking Study Using Graphical Data Representations

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Title: 'Seeing' Data Like an Expert: An Eye-Tracking Study Using Graphical Data Representations
Language: English
Authors: Harsh, Joseph A., Campillo, Molly, Murray, Caylin, Myers, Christina, Nguyen, John, Maltese, Adam V.
Source: CBE - Life Sciences Education. Sep 2019 18(3).
Availability: American Society for Cell Biology. 8120 Woodmont Avenue Suite 750, Bethesda, MD 20814-2762. Tel: 301-347-9300; Fax: 301-347-9310; e-mail: ascbinfo@ascb.org; Website: http://www.ascb.org
Peer Reviewed: Y
Page Count: 12
Publication Date: 2019
Sponsoring Agency: National Science Foundation (NSF)
Contract Number: 1346567
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Eye Movements, Data, Graphs, Undergraduate Students, Graduate Students, College Faculty, Biology, Attention, Comprehension, Nonmajors, Majors (Students), Expertise
Geographic Terms: Virginia
DOI: 10.1187/cbe.18-06-0102
ISSN: 1931-7913
Abstract: Given the centrality of data visualizations in communicating scientific information, increased emphasis has been placed on the development of students' graph literacy--the ability to generate and interpret data representations--to foster understanding of domain-specific knowledge and the successful navigation of everyday life. Despite prior literature that identifies student difficulties and methods to improve graphing competencies, there is little understanding as to how learners develop these skills. To gain a better resolution of the cognitive basis by which individuals "see" graphs, this study uses eye tracking (ET) to compare the strategies of non-science undergraduates (n = 9), early (n = 7) and advanced (n = 8) biology undergraduates, graduate students (n = 6), and science faculty (n = 6) in making sense of data displays. Results highlight variation in how individuals direct their attention (i.e., fixations and visual search patterns) when completing graph-based tasks as a function of science expertise. As research on the transition from novice to expert is crucially important in understanding how we might design curricula that help novices move toward more expert-like performance, this study has implications for the advancement of new strategies to aid the teaching and learning of data analysis skills.
Abstractor: As Provided
Entry Date: 2019
Accession Number: EJ1224765
Database: ERIC
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  Data: 'Seeing' Data Like an Expert: An Eye-Tracking Study Using Graphical Data Representations
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  Data: <searchLink fieldCode="SO" term="%22CBE+-+Life+Sciences+Education%22"><i>CBE - Life Sciences Education</i></searchLink>. Sep 2019 18(3).
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  Data: American Society for Cell Biology. 8120 Woodmont Avenue Suite 750, Bethesda, MD 20814-2762. Tel: 301-347-9300; Fax: 301-347-9310; e-mail: ascbinfo@ascb.org; Website: http://www.ascb.org
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  Data: Y
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  Data: 12
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  Data: 10.1187/cbe.18-06-0102
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  Data: 1931-7913
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  Data: Given the centrality of data visualizations in communicating scientific information, increased emphasis has been placed on the development of students' graph literacy--the ability to generate and interpret data representations--to foster understanding of domain-specific knowledge and the successful navigation of everyday life. Despite prior literature that identifies student difficulties and methods to improve graphing competencies, there is little understanding as to how learners develop these skills. To gain a better resolution of the cognitive basis by which individuals "see" graphs, this study uses eye tracking (ET) to compare the strategies of non-science undergraduates (n = 9), early (n = 7) and advanced (n = 8) biology undergraduates, graduate students (n = 6), and science faculty (n = 6) in making sense of data displays. Results highlight variation in how individuals direct their attention (i.e., fixations and visual search patterns) when completing graph-based tasks as a function of science expertise. As research on the transition from novice to expert is crucially important in understanding how we might design curricula that help novices move toward more expert-like performance, this study has implications for the advancement of new strategies to aid the teaching and learning of data analysis skills.
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        Value: 10.1187/cbe.18-06-0102
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      – Text: English
    PhysicalDescription:
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        PageCount: 12
    Subjects:
      – SubjectFull: Eye Movements
        Type: general
      – SubjectFull: Data
        Type: general
      – SubjectFull: Graphs
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      – SubjectFull: Graduate Students
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      – SubjectFull: Nonmajors
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      – SubjectFull: Expertise
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      – SubjectFull: Virginia
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      – TitleFull: 'Seeing' Data Like an Expert: An Eye-Tracking Study Using Graphical Data Representations
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