Automating human skills : preliminary development of a human factors methodology to capture tacit cognitive skills

Despite technological advances in intelligent automation, it remains difficult for engineers to discern which manual tasks, or task components, would be most suitable for transfer to automated alternatives. This research aimed to develop an accurate methodology for the measurement of both observable and unobservable physical and cognitive activities used in manual tasks for the capture of tacit skill. Experienced operators were observed and interviewed in detail, following which, hierarchical task analysis and task decomposition methods were used to systematically explore and classify the qualitative data. Results showed that a task analysis / decomposition methodology identified different types of skill (e.g. procedural or declarative) and knowledge (explicit or tacit) indicating this methodology could be used for further human skill capture studies. The benefit of this research will be to provide a methodology to capture human skill so that complex manual tasks can be more efficiently transferred into automated processes

Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation

There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions