Target Paper: Conception of the cognitive engineering design problem

Dowell, J. D.; Long, John

doi:10.1080/001401398187125

Cited by 79 publications

(44 citation statements)

References 16 publications

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“…DR and models provide abstract representations of knowledge and design trade-offs to support creative reasoning, while scenarios and prototypes give grounded examples from which to abstract more general principles. However, creative thought should generate innovative designs, but these need to be based on general principles; otherwise, design is limited to a craft-style incremental improvement of specific examples (Dowell & Long, 1998). I argue that a combination of representations is the most productive way to stimulate creative design, a challenge addressed in the next section.…”

Section: Affordances and Representationsmentioning

confidence: 99%

“…In contrast, the methodical engineering approach to design emphasizes a systematic process, models, and the reuse of design knowledge, criticizing less systematic approaches as "craft" (Dowell & Long, 1998). Design rationale may provide a middle ground between the two approaches as an easy-to-use notation that can stimulate creativity while preserving some of the generality and rigor of models.…”

Section: Introductionmentioning

confidence: 99%

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Juxtaposing Design Representations for Creativity

Sutcliffe¹

2010

Human Technology

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This paper argues that the influence of design rationale on creativity is best achieved by concurrent use of scenarios, prototypes and models. A framework of cognitive affordances is introduced to discuss the merits and limitations of each representation. The paper concludes by discussing how different representations might complement each other in creative scenario-based design.Keywords: scenarios, prototypes, cognitive affordances, design representations. INTRODUCTIONIt is often argued that creative design is best supported by examples of good design, thought probes, and stimulating artifacts (Cross, 2000;Gaver, Beaver, & Benford, 2003). In contrast, the methodical engineering approach to design emphasizes a systematic process, models, and the reuse of design knowledge, criticizing less systematic approaches as "craft" (Dowell & Long, 1998). Design rationale may provide a middle ground between the two approaches as an easy-to-use notation that can stimulate creativity while preserving some of the generality and rigor of models. I will investigate the contributions that different design representations can make to the creative design process from the viewpoint of cognitive reasoning processes. The relative merits of design rationale, scenarios, models, and prototypes are investigated in terms of their roles in the design process and cognitive affordances.The integrated use of different representations will be illustrated by the scenario-based requirements analysis method (SCRAM; Sutcliffe & Ryan, 1997). SCRAM advocates a combination of design rationale, scenarios, and early prototypes as a means of effective requirements analysis and design exploration. More recently we have used a merge of SCRAM and scenario-based design (Carroll, 2002) with a similar combination of design representations in eScience health informatics domains (Sutcliffe et al., 2007). The following section of this paper describes the properties of different design representations. Next, I discuss how the representations can support creative reasoning, with the following section elaborating the theme by investigating cognitive affordances. Then I review how representations can be integrated into the design requirements discovery process. Integration is illustrated with the SCRAM method, followed by a brief review of other approaches to creative design support. The paper DESIGN REPRESENTATIONSThis section reviews the role of the more common design representations in creative design from a human-computer interaction (HCI) perspective and from the more analytic view of software engineering.

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Section: Affordances and Representationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Juxtaposing Design Representations for Creativity

Sutcliffe¹

2010

Human Technology

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“…Graham et al, 2000;Trafton et al, 2000). This approach reflects the influence of the principles of cognitive engineering, where the work system (computer tools and user) is believed to be so tightly coupled to its domain that it does not make sense to evaluate performance on a visualization independent of a specific subject area (Dowell and Long, 1998). While this approach is appropriate for answering specific applied problems, the degree to which the results can be generalized to other applied areas is open to some question, in the sense that assumptions have to be made about the relationship between two specific domains.…”

Section: Evaluating Data Visualizationsmentioning

confidence: 99%

Visualizations of binary data: A comparative evaluation

Lee

Butavicius

Reilly

2003

International Journal of Human-Computer Studies

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Data visualization has the potential to assist humans in analysing and comprehending large volumes of data, and to detect patterns, clusters and outliers that are not obvious using nongraphical forms of presentation. For this reason, data visualizations have an important role to play in a diverse range of applied problems, including data exploration and mining, information retrieval, and intelligence analysis. Unfortunately, while various different approaches are available for data visualization, there have been few rigorous evaluations of their effectiveness. This paper presents the results of three controlled experiments comparing the ability of four different visualization approaches to help people answer meaningful questions for binary data sets. Two of these visualizations, Chernoff faces and star glyphs, represent objects using simple icon-like displays. The other two visualizations use a spatial arrangement of the objects, based on a model of human mental representation, where more similar objects are placed nearer each other. One of these spatial displays uses a common features model of similarity, while the other uses a distinctive features model. The first experiment finds that both glyph visualizations lead to slow, inaccurate answers being given with low confidence, while the faster and more confident answers for spatial visualizations are only accurate when the common features similarity model is used. The second experiment, which considers only the spatial visualizations, supports this finding, with the common features approach again producing more accurate answers. The third experiment measures human performance using the raw data in tabular form, and so allows the usefulness of visualizations in facilitating human performance to be assessed. This experiment confirms that people are faster, more confident and more accurate when an appropriate visualization of the data is made available. r

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“…For domains where the consequences of ineffective humantechnology interaction have serious potential outcomes for human life, being able to express whether or not particular interactions are effective, supports reasoning about the adequacy of the technology in question. Within the context of Cognitive Engineering, such expression and reasoning is termed 'diagnosis' (Dowell & Long, 1998;Rasmussen, 1986) and, as shall be illustrated in the paper, can support formulation of the design problem that a re-designed technology should solve (Dowell, 1998).…”

Section: Introduction I 1 Motivation and Overviewmentioning

confidence: 99%

“…The emphasis on 'design' and 'design problems (and solutions)' characterises the present approach as one of engineering, that is, contributing to the design of effective worksystems (Amalberti & Deblon, 1992;Dowell & Long, 1998;Flach, 1998;Hollnagel, 1998;Rasmussen, 1986;Reason, 1998;Vincente, 1998;Woods, 1998), rather than as one of science, that is, understanding the phenomena associated with worksystems and their behaviours (Barnard, 1991;Meyer & Kieras, 1999). Within the design approach, the present can be more precisely characterised as 'design for effectiveness', seeking to use the design primitives of 'work', 'worksystem', and 'performance' to motivate the acquisition and validation of design knowledge, to diagnose and solve design problems (in contrast, for example, to 'human performance', expressed as some form of speed and errors (Reason, 1998)).…”

Section: Introduction I 1 Motivation and Overviewmentioning

confidence: 99%