DLR Workload Assessment Tool (DLR-WAT)-Official English Version

Kurzfassung

This article presents an official English translation of the "DLR - Workload Assessment Tool" (DLRWAT), an originally German language questionnaire for subjective self-assessment of workload originally published in 2018. The DLR- WAT assesses deviations from a subjective optimum of workload more explicitly than existing measurement tools such as the NASA-TLX. The rationale behind the development of this tool is found in the increasing coexistence of humans and automation technology in sociotechnical systems across application domains such as transportation. Automation technology assists and takes over tasks formerly executed by humans as actors, resulting in changing human roles ranging for example from more passive monitoring tasks to short term interventions in cases of malfunction. In general, automation can relieve humans and increase their comfort. Yet, the issue of unbalanced workload and especially more prevalent underload needs to be targeted given the changing task environments faced by staff in the transportation domain. However, instruments for the subjective assessment of workload have so far lacked clear differentiation between underload and overload anchored in relation to a subjectively optimal level of workload. The DLR-WAT was developed to fill that gap, while greatly relying on the general format of the widely established NASATLX. The tool comprises a total of eight subscales. On six of the eight subscales (information acquisition, knowledge retrieval, decision-making, motor and physical demand, temporal demand, effort), the respondent can indicate his or her state of workload in relation to the personal optimum, which is located in the middle of each subscale. The two other subscales of the DLR-WAT (frustration, performance) are designed one-dimensionally, since an optimal level of frustration is characterised by the absence of frustration and the highest possible performance equals the theoretical optimum. The consideration of the personal optimum of workload in the first six subscales is thought to enable more detailed workload analyses distinctively imaging underload and overload in the areas represented by the subscales. In designing future transportation systems, this tool enables identification of the targeted balance between overload and underload across subscales and allows informed subsequent allocation of tasks between humans and automation accordingly.