Diagrams, Timelines, and Tables—Oh, My! Fostering Graphical Literacy

Roberts, Kathryn L.; Norman, Rebecca R.; Duke, Nell K.; Morsink, Paul M.; Martin, Nichole M.; Knight, Joseph F.

doi:10.1002/trtr.1174

Cited by 31 publications

(16 citation statements)

References 29 publications

(23 reference statements)

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“…Students are being exposed to complex graphics in print and digital spaces, and “the canon of devices that they are expected to interpret is a fast‐moving target” (Roberts et al, 2013, p. 21). In addition to traditional features such as captioned images, maps, flowcharts, and diagrams (Roberts, Brugar, & Norman, 2014), complex graphical representations such as infographics need to be considered as an increasingly prevalent component in nonfiction texts for K–12 students, especially in more recent texts and in digital environments.…”

Section: Comprehension Of Graphical Representationsmentioning

confidence: 99%

(Info)Graphically Inclined: A framework of infographic learning

Smith¹,

Robertson²

2021

The Reading Teacher

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Infographics are appearing in children’s magazines, picture books, and informational texts. Understanding, and ultimately creating, these complex visual representations of information or data requires higher level thinking skills to analyze and understand how the text and graphics work together to convey meaning. The authors provide a framework for infographic exploration, investigation, creation, and integration into larger writing pieces. Each phase of the framework includes specific considerations to assist teachers in scaffolding their students’ infographic learning. Providing students with support through exposure to many types of infographics and modeling infographic interpretation prepares students to critically examine and use infographics as they encounter them both in and out of school.

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Section: Comprehension Of Graphical Representationsmentioning

confidence: 99%

(Info)Graphically Inclined: A framework of infographic learning

Smith¹,

Robertson²

2021

The Reading Teacher

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“…Later, the concept was elaborated, and subdivided into the three skill levels "reading the data," "reading between the data," and "reading beyond the data" (Friel et al, 2001;Okan et al, 2012). Based on these theoretical considerations, fostering the development of graphical literacy became a focus of research (Gal, 2002;Shah and Hoeffner, 2002;Roberts et al, 2013). Furthermore, determining the cognitive variables underlying graphical literacy has also been of considerable interest.…”

Section: Structuring Individual Perception Of Data Visualization Withmentioning

confidence: 99%

“…Considering the associated commoditization, the user group for data analytics applications is not only becoming larger, but also more diverse, and so are personal backgrounds and levels of experience regarding data visualizations (Convertino and Echenique, 2017;Lennerholt et al, 2018). Although dealing with diversification is thus becoming more relevant, current research still focuses either on data-based recommendations for visualization generation (Viegas et al, 2007;Vartak et al, 2015;Wongsuphasawat et al, 2016) or on individual factors determining the processing of data visualizations (e.g., the data literacy concept) (Gal, 2002;Shah and Hoeffner, 2002;Roberts et al, 2013). However, these two important areas have not yet been sufficiently considered in conjunction, although the benefits of improving the accessibility of data through individualized visualizations may very tangibly contribute to achieving better business decisions.…”

Section: Introductionmentioning

confidence: 99%

Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Poetzsch

Germanakos

Huestegge

2020

Front. Artif. Intell.

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Data analytics as a field is currently at a crucial point in its development, as a commoditization takes place in the context of increasing amounts of data, more user diversity, and automated analysis solutions, the latter potentially eliminating the need for expert analysts. A central hypothesis of the present paper is that data visualizations should be adapted to both the user and the context. This idea was initially addressed in Study 1, which demonstrated substantial interindividual variability among a group of experts when freely choosing an option to visualize data sets. To lay the theoretical groundwork for a systematic, taxonomic approach, a user model combining user traits, states, strategies, and actions was proposed and further evaluated empirically in Studies 2 and 3. The results implied that for adapting to user traits, statistical expertise is a relevant dimension that should be considered. Additionally, for adapting to user states different user intentions such as monitoring and analysis should be accounted for. These results were used to develop a taxonomy which adapts visualization recommendations to these (and other) factors. A preliminary attempt to validate the taxonomy in Study 4 tested its visualization recommendations with a group of experts. While the corresponding results were somewhat ambiguous overall, some aspects nevertheless supported the claim that a user-adaptive data visualization approach based on the principles outlined in the taxonomy can indeed be useful. While the present approach to user adaptivity is still in its infancy and should be extended (e.g., by testing more participants), the general approach appears to be very promising.

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“…Schreier () recommended several steps, including building a coding frame, trial coding, and evaluating and modifying the coding. Initial decisions about our coding frame were based on research on nonfiction text and visual features (e.g., Bluestein, ; Kachorsky et al., ; Roberts et al., ; Serafini & Moses, ); Schreier () defined this as concept‐driven.…”

Section: Navigating the Texts: Our Processmentioning

confidence: 99%

Navigating Award‐Winning Nonfiction Children's Literature

Smith¹,

Robertson²

2019

The Reading Teacher

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Nonfiction children's literature has changed in recent years, including an increase in organizational, design, and text features. The authors conducted a content analysis of 112 nonfiction award‐winning and honor books from 2000 to 2018 to examine how the books have changed over time. The authors discuss the patterns, changes, and complexities found in nonfiction children's literature and provide text sets and guiding questions for classroom instruction and exploration of three features that appear in books in a variety of ways and may be challenging for students: atypical text, graphics with information, and supplemental expository information.

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Diagrams, Timelines, and Tables—Oh, My! Fostering Graphical Literacy

Cited by 31 publications

References 29 publications

(Info)Graphically Inclined: A framework of infographic learning

(Info)Graphically Inclined: A framework of infographic learning

Toward a Taxonomy for Adaptive Data Visualization in Analytics Applications

Navigating Award‐Winning Nonfiction Children's Literature

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