GOMS is a family of predictive models of human performance that can be used to improve the efficiency of human-machine interaction by identifying and eliminating unnecessary user actions. GOMS stands for (Goals, Operators, Methods, and Selection).
The simplest and most frequently used GOMS variant is KLM-GOMS< (Keystroke-Level Model), where empirically derived values for basic operators like keystrokes, button presses, double clicks, and pointer movement time, are used to estimate task times.
The other three major GOMS variants (CMN-GOMS, NGOMSL, and CPM-GOMS) require extensive training and familiarity with Human-Computer Interaction principles to perform an analysis.
Related Links
Baumeister, L.K., John, B.E., & Byrne, M.D. (2000). A comparison of tools for building GOMS Models Tools for Design. In Proc. of ACM Conf. on Human Factors in Computing Systems CHI’2000 (The Hague, 1-6 April 2000). ACM Press, New York. 502–509.
Gray, W. D., & John, B. E. (1993).
Project Ernestine: Validating a GOMS Analysis for Predicting and Explaining Real-World Task Performance.<
John, B. E., & Kieras, D. E. (1996).
Using GOMS for User Interface Design and Evaluation: Which Technique?< This article describes the different GOMS techniques and when they should be used.
Kieras, D. E. (2006)
A Guide to GOMS Model Usability Evaluation using GOMSL and GLEAN4< This
document is a heavily modified version of the earlier "Guides" to GOMS modeling, Kieras (1988, 1997a), and supersedes the 1999 Guide referring to GLEAN3. It contains detailed information about GOMS, its strengths and limitations, how to construct a GOMS model with examples, and how to use a GOMS model to predict human performance.
Facts
Sources and contributors:
Costin Pribeano and Georgios Christou (as part of MAUSE), Ben Werner.