The Correlation between Users’ Cognitive Characteristics and Visualization Literacy

Lee, Sung‐Hee; Kwon, Bum Chul; Yang, Jincheng; Lee, Byung Cheol; Kim, Sunghee

doi:10.3390/app9030488

Cited by 23 publications

(18 citation statements)

References 62 publications

(118 reference statements)

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“…The research by Lee et al 28 aims to find the connection between visual literacy and the following three cognitive characteristics: numeracy as cognitive ability, cognitive motivation and cognitive style. An experiment with 178 participants using MTurk is conducted.…”

Section: The State-of-the-art On Interactive Visualization Literacymentioning

confidence: 99%

“…Baker et al 34 R + W A 52 Delmas et al 35 R A 1464 Scho ¨nborn et al 36 R A + C N/A Grammel et al 25 R + W A 4, 9 Kodagoda et al 26 R A 24 Boy et al 4 R A 34, 37, 34, 39 Huron et al 37 R + W A 12 Ruchikarhorn and Mueller 38 R A 22, 11,11 Maltese et al 39 R A 202 Kwon and Lee 40 R + W A 120 Bo ¨rner et al 5 R A + C 273 Alper et al 41 R + W C 6, 15 Lee et al 6 R A 65, 191 Wojton et al 42 R A 388 Chevalier et al 1 R + W C N/A Zoss et al 43 R + W A+ C N/A Mansoor and Harrison 44 R A + C N/A Bo ¨rner et al 45 R + W A+ C N/A Stoiber et al 46 R A + C N/A Lee et al 28 R A 178 Fuchs et al 47 R A 28 Ga ¨bler et al 48 R C 23 Bishop et al 27 R C 24 Lalle ´et al 49 R A 119 Krekhov et al 50 R + W A 11 Firat et al 22 R A 25 Wang et al 51 R + W A 11 Rodrigues et al 52 R A 22 Huynh et al 53 R C 33 D'Ignazio and Bhargava 54 R + W A + C N/A Donohoe and Costello 55 R A 32 Barral et al 56 R A 56, 119 Barral et al 57 R A 56, 119 Peppler et al 58 R The first in the wild concentrates on the process of collaborating with community groups, Groundwork Somerville, and local youth design to paint a datadriven story as a community mural. This example of a ''data mural'' documents an action-oriented, community-based project that builds data literacy.…”

Section: Number Of Participantsmentioning

confidence: 99%

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Interactive visualization literacy: The state-of-the-art

Fırat

Joshi²,

Laramee

2022

Information Visualization

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With the advent of novel visualization techniques to convey complex information, data visualization literacy is growing in importance. Two facets of literacy are user understanding and the discovery of visual patterns with the help of graphical representations. The research literature on visualization literacy provides useful guidance and important opportunities for further studies in this field. This survey examines and classifies prior research on visualization literacy that analyzes how well users understand novel data representations. To our knowledge, this is the first comprehensive survey paper with a focus on interactive visualization literacy. We categorize existing relevant research into unique subject groups that facilitate and inform comparisons of related literature and provide an overview of the same. Additionally, the survey/classification also provides an overview of the various evaluation techniques used in this field of research due to their challenging nature. Our novel classification enables researchers to find both mature and unexplored directions that may lead to future work. This survey serves as a valuable resource for both beginners and experienced researchers interested in the topic of visualization literacy.

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Section: The State-of-the-art On Interactive Visualization Literacymentioning

confidence: 99%

Section: Number Of Participantsmentioning

confidence: 99%

Interactive visualization literacy: The state-of-the-art

Fırat

Joshi²,

Laramee

2022

Information Visualization

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“…With average SUS score of 84.8%, the experts' rating demonstrates and validates the effectiveness of the design in visualizing OBE predictive assessment. The inclusion of visualization literacy into the design consideration greatly affects the perceived effectiveness of presented visualization to the users [28]. Despite the positive results, the implemented design only employs basic visualization that can be limited when visualizing advanced or complex analysis scenario.…”

Section: B Discussionmentioning

confidence: 99%

OBEInsights: Visual Analytics Design for Predictive OBE Knowledge Generation

Lumius¹,

Asli²

2021

IJACSA

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Gaining traction in modern higher education, outcome-based education (OBE) focuses on strategizing pedagogical approaches to help the student achieve specified learning outcomes. In the context of Malaysia, OBE is oriented towards holistic development of graduates to ensure readiness towards the working sector. To empower OBE implementation, standardized measuring instrument iCGPA was introduced to higher education institutions nationwide. With lower dependency on provided curriculum, graduate abilities and values development are also attainable via extracurricular activities. However, analyzing the curriculum results in hand with extracurricular activities can be a daunting task, albeit the potential enriched performance assessment. In addition, the current iCGPA instrument employs radar map that restricts data exploration despite its capability in visualizing multivariate information. This study aims to enable predictive knowledge generation on understanding the relationship between learning activities and performance in OBE. Therefore, a predictive visual analytics system namely OBEInsights is proposed to facilitate education analysts in assessing OBE. The system development started with the identification of crucial design and analytic requirements via a domain expert case study. The system is then built with deliberate considerations of guiding factors of a design framework conceptualized from the case study. Subsequently, the system was then evaluated in usability testing with 10 domain experts that consist of usability rating and expert validation. The evaluation and expert validation results demonstrated the effectiveness and usability of OBEInsights in facilitating OBE predictive assessment. Several design insights on constructing visual analytics for OBE assessment were also discovered in terms of effective visualization, predictive modeling, and knowledge generation. Analytic designers and builders can leverage the reported design insights to enhance knowledge generation tools for OBE assessment.

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“…Results from prior studies with children (see section 2.3) guide us to expect that children will have larger errors than adults on graphical perception tasks. We believe this because children's proportional reasoning skills are still maturing [8,46], their graphical perception skill is tied to numerous other cognitive abilities that may mature with more schooling [42], and they use different and sometimes less effective strategies than adults [36]. Our best indicator of a difference between children and adults is the work done by Spence & Krizel [65,66], which found consistently larger errors for children on proportional reasoning tasks.…”

Section: Hypothesesmentioning

confidence: 99%

Juvenile Graphical Perception: A Comparison between Children and Adults

Panavas¹

2022

Preprint

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Data visualization is pervasive in the lives of children as they en-counter graphs and charts in early education and online media.In spite of this prevalence, our guidelines and understanding ofhow children perceive graphs stem primarily from studies con-ducted with adults. Previous psychology and education researchindicates that children’s cognitive abilities are different from adults.Therefore, we conducted a classic graphical perception study on apopulation of children aged 8–12 enrolled in the Ivy After SchoolProgram in Boston, MA and adult computer science students en-rolled in Northeastern University to determine how accuratelyparticipants judge differences in particular graphical encodings. We record the accuracy of participants’ answers for five encodingsmost commonly used with quantitative data. The results of ourcontrolled experiment show that children have remarkably similargraphical perception to adults, but are consistently less accurateat interpreting the visual encodings. We found similar effective-ness rankings, relative differences in error between the differentencodings, and patterns of bias across encoding types. Based on ourfindings, we provide design guidelines and recommendations forcreating visualizations for children. This paper and all supplementalmaterials are available at https://osf.io/ygrdv.

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The Correlation between Users’ Cognitive Characteristics and Visualization Literacy

Cited by 23 publications

References 62 publications

Interactive visualization literacy: The state-of-the-art

Interactive visualization literacy: The state-of-the-art

OBEInsights: Visual Analytics Design for Predictive OBE Knowledge Generation

Juvenile Graphical Perception: A Comparison between Children and Adults

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