P-Lite: A study of parallel coordinate plot literacy

Fırat, Elif E.; Denisova, Alena; Wilson, Max L.; Laramee, Robert S.

doi:10.1016/j.visinf.2022.05.002

Cited by 7 publications

(13 citation statements)

References 17 publications

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“…Several research works have been proposed towards understanding and measuring visualization literacy [GGR11, FDWL22, FDL20, LKK16, KBC01, BMBH16]. Most early works have measured the ability to read and interpret a graph [GGR11,Ber10,Car92,Wai92].…”

Section: Related Workmentioning

confidence: 99%

“…After they finished the first section, we introduced them to six static tutorials on Parallel Coordinate Plot before proceeding to the second section [KL16]. In the second section, we asked the participants to answer the 14 items obtained from the Parallel Coordinate Plot test, P‐Lite [FDWL22]. The questions in both sections were randomized.…”

Section: Phase V: Testing Mini‐vlat's Predictive Capacitymentioning

confidence: 99%

“…The test was also validated among five visualization experts. The VLAT encouraged researchers to extend the work by adding more advanced visual designs [FDL20, FDWL22]. Firat et al [FDWL22] developed a parallel coordinates literacy test, P‐Lite, with diverse images generated using popular PCP software tools.…”

Section: Related Workmentioning

confidence: 99%

“…The VLAT encouraged researchers to extend the work by adding more advanced visual designs [FDL20, FDWL22]. Firat et al [FDWL22] developed a parallel coordinates literacy test, P‐Lite, with diverse images generated using popular PCP software tools. Like VLAT, P‐Lite was created to test the user's comprehension and ability to interpret high‐dimensional visual designs like parallel coordinates.…”

Section: Related Workmentioning

confidence: 99%

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Mini‐VLAT: A Short and Effective Measure of Visualization Literacy

Saugat

Ottley

2023

Computer Graphics Forum

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The visualization community regards visualization literacy as a necessary skill. Yet, despite the recent increase in research into visualization literacy by the education and visualization communities, we lack practical and time‐effective instruments for the widespread measurements of people's comprehension and interpretation of visual designs. We present Mini‐VLAT, a brief but practical visualization literacy test. The Mini‐VLAT is a 12‐item short form of the 53‐item Visualization Literacy Assessment Test (VLAT). The Mini‐VLAT is reliable (coefficient omega = 0.72) and strongly correlates with the VLAT. Five visualization experts validated the Mini‐VLAT items, yielding an average content validity ratio (CVR) of 0.6. We further validate Mini‐VLAT by demonstrating a strong positive correlation between study participants' Mini‐VLAT scores and their aptitude for learning an unfamiliar visualization using a Parallel Coordinate Plot test. Overall, the Mini‐VLAT items showed a similar pattern of validity and reliability as the 53‐item VLAT. The results show that Mini‐VLAT is a psychometrically sound and practical short measure of visualization literacy.

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Section: Related Workmentioning

confidence: 99%

Section: Phase V: Testing Mini‐vlat's Predictive Capacitymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Mini‐VLAT: A Short and Effective Measure of Visualization Literacy

Saugat

Ottley

2023

Computer Graphics Forum

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“…Fully active approaches onboard users while interacting with a visualization, for instance, by enabling two‐way communication [EAB13,BE18], or through context‐aware annotations [EB12, KPHH12]. Active approaches have been shown to be more effective than passive ones in specific cases [FDL20,KL16].…”

Section: Related Workmentioning

confidence: 99%

A Process Model for Dashboard Onboarding

Dhanoa

Walchshofer

Hinterreiter

et al. 2022

Computer Graphics Forum

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Dashboards are used ubiquitously to gain and present insights into data by means of interactive visualizations. To bridge the gap between non‐expert dashboard users and potentially complex datasets and/or visualizations, a variety of onboarding strategies are employed, including videos, narration, and interactive tutorials. We propose a process model for dashboard onboarding that formalizes and unifies such diverse onboarding strategies. Our model introduces the onboarding loop alongside the dashboard usage loop. Unpacking the onboarding loop reveals how each onboarding strategy combines selected building blocks of the dashboard with an onboarding narrative. Specific means are applied to this narration sequence for onboarding, which results in onboarding artifacts that are presented to the user via an interface. We concretize these concepts by showing how our process model can be used to describe a selection of real‐world onboarding examples. Finally, we discuss how our model can serve as an actionable blueprint for developing new onboarding systems.

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