Human factors in visualization research

Tory, Melanie; Möller, Torsten B.

doi:10.1109/tvcg.2004.1260759

Cited by 324 publications

(225 citation statements)

References 59 publications

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“…Many researchers have proposed the use of inspection methods as part of their evaluation methodology for information visualization systems [23,25,34,35,38]. In general, there are two styles of inspection method: those that are based on a set of design guidelines, such as heuristic evaluations; and those that are based on performing specific tasks, such as cognitive walkthroughs.…”

Section: Inspection Methodsmentioning

confidence: 99%

“…The use of laboratory studies is promoted in most papers on the topic of evaluating information visualization systems [1,4,7,21,23,25,26,34]. In general, problems with such studies include the fact that they are time consuming, expensive, and difficult to design [7,34], and that participants are observed for a short period of time [23].…”

Section: Laboratory Studiesmentioning

confidence: 99%

“…In general, problems with such studies include the fact that they are time consuming, expensive, and difficult to design [7,34], and that participants are observed for a short period of time [23]. Such studies often make use of students as participants, even though they may not accurately reflect the target user group [1,25].…”

Section: Laboratory Studiesmentioning

confidence: 99%

“…Such studies often make use of students as participants, even though they may not accurately reflect the target user group [1,25]. A further critique of laboratory studies is that they focus on the abilities of novice users to quickly learn and use the system under investigation [34]. Since the design of laboratory studies is outside of the scope of this paper, we refer the reader to Andrews [1], Carpendale [4], and Ellis & Dix [7] for practical advice regarding the evaluation of information visualization systems in a laboratory setting.…”

Section: Laboratory Studiesmentioning

confidence: 99%

“…This work builds upon the research by others on the topic of evaluating information visualization systems [1,4,7,16,23,25,26,29,34]. We present a cross-section of evaluation methods available for researchers to validate their designs for visual Web search interfaces.…”

Section: Introductionmentioning

confidence: 99%

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User Evaluation Methods for Visual Web Search Interfaces

Hoeber

2009

2009 13th International Conference Information Visualisation

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In recent years, numerous visual Web search interfaces have been developed in the research community. However, the user evaluations of these interfaces have been performed using a wide range of methods, making it difficult to compare and verify the relative value of the proposed advancements. In this paper, we survey these evaluation methods, and propose a stepped evaluation and refinement model for the systematic study and enhancement of visual Web search interfaces. We suggest that this stepped model can be generalized to support the evaluation of other information visualization systems that target exploratory or knowledge-centric domains.

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Section: Inspection Methodsmentioning

confidence: 99%

Section: Laboratory Studiesmentioning

confidence: 99%

Section: Laboratory Studiesmentioning

confidence: 99%

Section: Laboratory Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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User Evaluation Methods for Visual Web Search Interfaces

Hoeber

2009

2009 13th International Conference Information Visualisation

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Visualizing Uncertainty

Zuk

2011

Modeling Uncertainty in the Earth Sciences

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Uncertainty is a normal part of everyday life. It appears in the environment around us from the weather to the stock market, internally to some degree in almost every plan or decision we make, and is inherent in our daily communication, both verbal and visual. The form this uncertainty takes is often qualitative or unquantified and so fits poorly with the initial issues of representation, computability, and efficiency often the driving forces in initial visualizations of information. Understanding what may assist in visualizing uncertainty is the subject of this research. Initially I provide a literature review of existing work in uncertainty visualization. This review continues with an exploration of heuristic evaluation specifically on uncertainty visualization but then looks deeper at the process of heuristic evaluation itself. Moving toward user constraints and cognitive tasks I coalesce existing work relating to reasoning under uncertainty. From this I propose further linking and integrating the uncertainty visualizations into the process of reasoning which encompasses all visualization tasks. The second half of the dissertation turns to investigate uncertainty visualization in spe cific domains. In the first domain, results of research into visualizing temporal uncertainty in archaeological reconstructions are provided. This is followed by visualizations devel oped for uncertainty in rock property modelling in the seismic domain. The final domain of evidence-based medical diagnosis is explored with an observational study, participatory design of new visual support, and a final evaluation. Finally I present a framework for assisting with the development of visualizations deal ing with uncertainty by breaking out several important factors and cognitive tasks to con sider based on generalizing and applying the practical and theoretical developments. In summary my contributions include specific visualizations for particular application do mains along with more general aspects relating to evaluation, applicability of cognitive theory, and a framework to aid uncertainty visualization. iii This dissertation is the result of the work and support of many people. First and foremost I would like to thank all of my family for all their patience and support. I deeply thank my supervisor Sheelagh Carpendale for all the direction, time, and effort; all my domain collaborators for their contributions-Bill Glanzman for the archaeological discussions over beer, Bill Ghali and Barry Baylis and the the rest of the Ward of the 21 st Century research group for their insights and ideas, and Jon Downton and Dave Gray for their anisotropic discussions; and all my other co-authors for their contribution: Petra Isenberg,

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When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care

Meulen

Logie

Freer

et al. 2008

Applied Cognitive Psychology

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Volunteer staff from a Neonatal Intensive Care Unit (NICU) were presented with sets of anonymised physiological data recorded over approximately 45 minute periods from former patients. Staff were asked to select medical/nursing actions appropriate for each of the patients whose data were displayed. Data were shown in one of three conditions (a) as multiple line graphs similar to those commonly shown on the ward, or as textual descriptions generated by (b) expert medical/nursing staff or (c) computerised natural language generation (NLG). An overall advantage was found for the human generated text, but NLG resulted in decisions that were at least as good as those for the graphical displays with which staff were familiar. It is suggested that NLG might offer a viable automated approach to removing noise and artefacts in real, complex and dynamic data sets, thereby reducing visual complexity and mental workload, and enhancing decision-making particularly for inexperienced staff.

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Human factors in visualization research

Cited by 324 publications

References 59 publications

User Evaluation Methods for Visual Web Search Interfaces

User Evaluation Methods for Visual Web Search Interfaces

Visualizing Uncertainty

When a graph is poorer than 100 words: A comparison of computerised natural language generation, human generated descriptions and graphical displays in neonatal intensive care

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