Mental Workload Prediction Method Based on GOMS

Abstract

The conventional approaches to assessing mental workload with operators are time-consuming, and even more challenging when experienced operators are tougher to find. Prior to an experiment involving operators, mental workload prediction methods may be useful for having a preliminary evaluation of a system or interface. This study represented mental workload using the ratio of GOMS-based predicted task completion time to available time. The low-version and high-version maritime operation interfaces were compared. In the GOMS analysis, this study disassembled task goals based on a hierarchical structure and matched each subtask goal with a method. Given the presence of considerable repetitive GOMS operators throughout the task execution, the idea of operator sequence block was introduced for task analysis and reader comprehension. By nesting these blocks, the task was decomposed into keystroke-level GOMS operators. By accumulating the standard times of the GOMS operators, the time prediction results for operator sequence blocks, methods, hierarchical task goals, and overall task can be obtained following the bottom-up approach.The results indicated that the number of GOMS operators and the task completion time required for the operators significantly decreased when using the high-version interface. Consequently, it was anticipated that the high-version interface could notably reduce the operators’ mental workload. The mental workload prediction method based on GOMS proposed in this study can be used to guide early-stage interface design to enhance operator performance.

Keywords: GOMS, Mental workload, Prediction

DOI: 10.54941/ahfe1004861