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KLM-GOMS×Cognitive Walkthrough×
DziedzinaInterakcja człowiek–komputerInterakcja człowiek–komputer
RodzinaHypothesis testHypothesis test
Rok powstania19831990
TwórcaStuart Card, Thomas Moran, Allen NewellClayton Lewis, Peter Polson, Cathleen Wharton, John Rieman
TypComputational cognitive model for task execution time predictionEvaluative walkthrough examining how users learn to use an interface
Źródło pierwotneCard, S. K., Moran, T. P., & Newell, A. (1983). The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates. ISBN: 0898592437Lewis, C., Polson, P. G., Wharton, C., & Rieman, J. (1990). Testing a walkthrough methodology for specifying and evaluating user interface designs. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 387–392). link ↗
Inne nazwyGOMS Model, KLMCognitive Walkthrough, CW Analysis
Pokrewne44
PodsumowanieThe Keystroke-Level Model (KLM), part of the Goals-Operators-Methods-Selection rules (GOMS) framework, is a computational method for predicting how long a user will take to accomplish a routine task using an interactive system. Developed by Card, Moran, and Newell in 1983, KLM decomposes user actions into primitive operators (keystrokes, mouse clicks, mental preparation, system response waits) with empirically derived execution times, enabling designers to estimate task performance without running user studies.Cognitive Walkthrough is an inspection method for evaluating interface designs by simulating and analyzing how users will learn to use a system through exploration and trial. Developed by Clayton Lewis, Peter Polson, Cathleen Wharton, and John Rieman in 1990, this method is grounded in cognitive psychology and focuses specifically on learnability—whether first-time or occasional users can discover how to perform tasks without formal training. Evaluators role-play user actions, answer a set of critical questions about feedback and discovery at each step, and document usability problems.
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ScholarGatePorównaj metody: KLM-GOMS · Cognitive Walkthrough. Pobrano 2026-06-18 z https://scholargate.app/pl/compare