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  1. Ann Heylighen & Matteo Bianchin (2013). How Does Inclusive Design Relate to Good Design? Designing as a Deliberative Enterprise. Design Studies 34 (1):93-110.
    Underlying the development of inclusive design approaches seems to be the assumption that inclusivity automatically leads to good design. What good design means, however, and how this relates to inclusivity, is not very clear. In this paper we try to shed light on these questions. In doing so, we provide an argument for conceiving design as a deliberative enterprise. We point out how inclusivity and normative objectivity can be reconciled, by defining the norm of good design in terms of a (...)
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  2. Ann Heylighen, Humberto Cavallin & Matteo Bianchin (2009). Design in Mind. Design Issues 25 (1):94-105.
  3. Humberto Cavallin, W. Mike Martin & Ann Heylighen (2007). How Relative Absolute Can Be: SUMI and the Impact of the Nature of the Task in Measuring Perceived Software Usability. [REVIEW] AI and Society 22 (2):227-235.
    This paper addresses the possibility of measuring perceived usability in an absolute way. It studies the impact of the nature of the tasks performed in perceived software usability evaluation, using for this purpose the subjective evaluation of an application’s performance via the Software Usability Measurement Inventory (SUMI). The paper reports on the post-hoc analysis of data from a productivity study for testing the effect of changes in the graphical user interface (GUI) of a market leading drafting application. Even though one (...)
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  4. Ann Heylighen, Francis Heylighen, Johan Bollen & Mathias Casaer (2007). Distributed (Design) Knowledge Exchange. AI and Society 22 (2):145-154.
    Despite the intrinsic complexity of integrating individual, social and technologically supported intelligence, the paper proposes a relatively simple ‘connectionist’ framework for conceptualizing distributed cognitive systems. Shared information sources (documents) are represented as nodes connected by links of variable strength, which increases as the documents co-occur in the usage patterns. This learning procedure captures and exploits its users’ implicit knowledge to help them find relevant information, thus supporting an unconscious form of exchange. These principles are applied to a concrete problem domain: (...)
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