Quick and Dirty GOMS: A Case Study of Computed Tomography Interpretation
| Autor: | Kevin M. Denelsbeck, Dana K. Smith, David V. Beard |
|---|---|
| Rok vydání: | 1996 |
| Předmět: |
Interpretation (logic)
medicine.diagnostic_test business.industry Interface (Java) Computer science Quick-and-dirty Computed tomography Machine learning computer.software_genre Human-Computer Interaction Tree (data structure) Software GOMS Selection (linguistics) medicine Artificial intelligence business computer Applied Psychology |
| Zdroj: | Human-Computer Interaction. 11:157-180 |
| ISSN: | 0737-0024 |
| Popis: | GOMS (goals, operators, methods, and selection rules) has the potential for a high degree of theoretical accuracy. However, this accuracy is time-consuming to realize under field conditions, and, consequently, GOMS generally is not used by software engineers. Nevertheless, in our experience, GOMS models can be practical if the effort required to produce and use the model is commensurate with its limited practical accuracy. To speed and simplify the development of GOMS models, we developed "quick and dirty" GOMS-or QGOMS. In this work, we detail an 8-year case study in which QGOMS models were used by a software engineering team to develop and evolve an interface for the electronic display of computed tomography (CT) medical images. Experimentally generated timing results were compared to model estimates to provide an indication of expected accuracy for QGOMS models under field conditions. We have also implemented a direct-manipulation graphical tree editor that allows rapid development and analysis of QGOMS models. In addition to rapid model construction, QGOMS provides "probability selection rules" allowing a more refined GOMS analysis. |
| Databáze: | OpenAIRE |
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