Organization and learnability in computer languages
International Journal of Man-Machine Studies - Lecture notes in computer science Vol. 174
A test of a common elements theory of transfer
CHI '86 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Cognitive resources and the learning of human-computer dialogs
Interfacing thought: cognitive aspects of human-computer interaction
A quantitative theory of human-computer interaction
Interfacing thought: cognitive aspects of human-computer interaction
CHI '87 Proceedings of the SIGCHI/GI Conference on Human Factors in Computing Systems and Graphics Interface
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
A quantitative model of the learning and performance of text editing knowledge
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Architecture of Cognition
The Psychology of Human-Computer Interaction
The Psychology of Human-Computer Interaction
A generalized transition network representation for interactive systems
CHI '83 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Human Problem Solving
Human-computer interaction design with multi-goal facilities layout model
Computers & Mathematics with Applications
Towards analytical evaluation of human machine interfaces developed in the context of smart homes
Interacting with Computers
Determination of optimal paths to task goals using expert system based on GOMS model
Computers in Human Behavior
International Journal of Human-Computer Studies
Hi-index | 0.00 |
Kieras and Polson (1985) proposed an approach for making quantitative predictions on ease of learning and ease of use of a system, based on a production system version of the goals, operators, methods, and selection rules (GOMS) model of Card, Moran, and Newell (1983). This article describes the principles for constructing such models and obtaining predictions of learning and execution time. A production rule model for a simulated text editor is described in detail and is compared to experimental data on learning and performance. The model accounted well for both learning and execution time and for the details of the increase in speed with practice. The relationship between the performance model and the Keystroke-Level Model of Card et al. (1983) is discussed. The results provide strong support for the original proposal that production rule models can make quantitative predictions for both ease of learning and ease of use.