Updated Goals, Operators, Methods, and Selection Rules (GOMS) with Touch Screen Operations for Quantitative Analysis of User Interfaces

Abstract

When developing applications, User Interfaces (UI) are considered as an important and integral component of any application. With badly designed UI, users are less likely to use the application, leading to low adoption rates and is not desirable in any application development setting. The process of developing good intuitive and stream-lined UI for users is complex, that requires many processes and experts from many fields to contribute. When evaluating potential UI designs, there are many attributes and features that could be examined either in a qualitative and/or quantitative standpoint. The Updated Goals, Operators, Methods, and Selection Rules (GOMS) model is an approach that has been used in the area. With the Keystroke Level (KLM) extension, it is possible to quantitatively estimate the time requirement or efficiency of UI for completing different tasks with minimal effort, and has been adopted in many GUI improvement projects. Due to the usefulness, extensions to the GOMS model had been proposed over the years including extensions to account for motion control interfaces. Though the GOMS model has been useful for quantitatively evaluating UI design, the main input device of modern smartphones and tablets are touch screens, which are different in nature when compared to traditional computer inputs. The differences leads to the point that GOMS model with KLM is ill-suited for touch screen based applications. This research paper addresses the issue by proposing extensions to the GOMS model to account for UI that are based on touch screen input devices.

An execution time prediction model for crew information processing in new special vehicles

“…(a) Redefinition of original operators  K_S: Finger with touch screen to complete pressing and lifting sub-operation units instantaneously. The average  time of this operator was 0.2s obtained by Setthawong et al [13].…”

Estimating Cognitive Load in a Mobile Personal Health Record Application: A Cognitive Task Analysis Approach

“…Lastly, two proposed GOMS analysis techniques-the updated GOMS model [20] and the gesture-level model [22]were applied to estimate the execution times of the 10 prototypical tasks. As seen in Table 2, the times of these operators were recorded separately for both models and added.…”

“…This study used the GOMS model to analyze the cognitive complexity of the e-Nabız mPHR application. Two proposed GOMS analysis techniques-the updated GOMS model [20] and the gesture-level model [22,23]-were applied to estimate the execution times of the determined prototypical tasks using the device Samsung Note10+ (Android OS, screen size of 6.8 inches/17.27 cm). A mental operator and three physical operators were used, as suggested in these models (Table 1), since these are sufficient to evaluate the e-Nabız application.…”

“…Research [18,19] has shown that scrutinizing the fundamental task steps can help eliminate unnecessary processes and streamline task execution for optimal efficiency. This model enables designers to quantitatively estimate the time required or the efficiency of a given user interface for a specific task by breaking down the user's steps and summing the execution time of each step, all without conducting detailed user tests [20]. Goals frequently comprise sub-goals, representing users' intentions for using the system.…”