An Approach to the Formal Analysis of User Complexity

By D. Kieras, Peter G. Polson. 1999

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, methodology): “ther inferences. User models have been used to support the "tailoring" of explanations in a number of ways (cf. Carberry, 1990 for a discussion of a 1990 workshop; see also Kay, 2000; 2006, Kieras and Polson, 1985; May, Barnard, and Blandford, 1993; Maida and Deng, 1989; Weiner, 1989) User”
A survey of software learnability: metrics, methodologies and guidelines (2009, unknown): “[19, 24]”
Human Factors and Ergonomics (2011, unknown): “, 2004), SOAR (Newell, 1990), CCT (Kieras and Polson, 1985) and EPIC (Meyer and Kieras, 1997).”
MENTAL MODELS IN HUMAN-COMPUTER INTERACTION (2010, background): “Proposed notations include grammars (e.g. Moran 1981, Payne and Green 1986, Reisner 1984), production rules (Kieras and Polson 1985), and stacks (Card, Moran, and Newell 1983).”
The Effects of Progressive Levels of Interactivity and Vividness in Web Marketing Sites (2001, background): “Individual differences in experience with comput erized media have been shown to affect attitudes to ward computer programs (Goldstein and Ford 1978; Kieras and Polson 1985; Rich 1983; Vincente, Hayes, and Williges 1987), and researchers have warned about the confounding potential of such…”
Toward preprototype user acceptance testing of new information systems: implications for software project management (2004, background): “Kieras and Polson [41] suggest that users possess distinct knowledge about their job situation, which they”
Mental Models: Theory and Application in Human Factors (1989, methodology): “Several notations have been employed to describe this mapping, such as grammars (e.g., Moran, 1981; Payne and Green, 1986; Reisner, 1984), production rules (Kieras and Polson, 1985), and stacks (Card, Moran, and Newell, 1983).”
The state of the art in automating usability evaluation of user interfaces (2001, methodology): “The surveyed methods return a number of simulation results, including predictions of task performance (EPIC, CCT, COGNET, GLEAN, HOS, Soar, ACT-R), memory load (ICS, CCT), learning (ACT-R, Soar, ICS, GLEAN, CCT), or behavior predictions such as action traces (ACT-R, COGNET, EPIC).”
Need for multi-aspect measures to support evaluation of complex human-computer interfaces (1998, background): “Examples include the mapping of device and task representations [15], the definitions of "semantic distance" and "articulatory distance" in interface language [11], and the measure of "visual momentum" [33].”
Structures : Analysis , Modelling and Application (2007, unknown): “Neither [2 ] nor [10] explicitly claim that people actually possess these task-knowledge structures.”
State of the Art on the Cognitive Walkthrough Method, Its Variants and Evolutions (2010, background): “If the goal is achieved, the learning that occurs is registered by inscribing the steps taken by the system (i.e., the evaluated response) in the rule-based representation of procedural knowledge proposed by Kieras (1985).”