Complex industrial systems, including mining, have a prominent challenge in understanding the interrelationship among the cognitive processes, working environment and available equipment. The concept of cognitive work...Complex industrial systems, including mining, have a prominent challenge in understanding the interrelationship among the cognitive processes, working environment and available equipment. The concept of cognitive work analysis(CWA) transcends the traditional analytic methods of evaluating human tasks solely based on perceptual and physical traits, and rather implements the notions of behavioral and cognitive awareness indispensable for the intricacy of modern technology. In the last few decades, academic and industrial settings employ this type of analysis to set a suitable standard for a system's safety feasibility, and as a result reduce human-based errors. This research paper analyzes current CWA methods and proposes a five-level quantification model portraying the overall cognitive quality of a mining operation.展开更多
The design with intent(DwI)toolkit assists designers in creating novel designs and interfaces.DwI,however,is not constrained to any degree,making it impossible to know whether the produced designs adequately account f...The design with intent(DwI)toolkit assists designers in creating novel designs and interfaces.DwI,however,is not constrained to any degree,making it impossible to know whether the produced designs adequately account for users’needs.In contrast,cognitive work analysis(CWA)is a human factors research tool that seeks to map a system and account for users’needs,yet does not provide clear guidelines for progressing such analysis into workable designs with which users can interact.This paper seeks to present a proof-of-concept investigation to demonstrate that DwI can be suitably constrained and validated by insights gained from CWA.CWA,in turn,benefits by having a suitable toolkit for progressing insights.Two teams of individuals without design backgrounds were able to develop mock-up in-vehicle interfaces aimed at reducing fuel use.The teams were able to use DwI toolkit to articulate the genesis of their ideas,which in turn could be directly linked to system needs identified within CWA.展开更多
文摘Complex industrial systems, including mining, have a prominent challenge in understanding the interrelationship among the cognitive processes, working environment and available equipment. The concept of cognitive work analysis(CWA) transcends the traditional analytic methods of evaluating human tasks solely based on perceptual and physical traits, and rather implements the notions of behavioral and cognitive awareness indispensable for the intricacy of modern technology. In the last few decades, academic and industrial settings employ this type of analysis to set a suitable standard for a system's safety feasibility, and as a result reduce human-based errors. This research paper analyzes current CWA methods and proposes a five-level quantification model portraying the overall cognitive quality of a mining operation.
基金funded by the UK Engineering and Physical Sciences Research Council(EPSRC)Grant EP/N022262/1“Green Adaptive Control for Future Interconnected Vehicles”(www.gactiv e.uk).
文摘The design with intent(DwI)toolkit assists designers in creating novel designs and interfaces.DwI,however,is not constrained to any degree,making it impossible to know whether the produced designs adequately account for users’needs.In contrast,cognitive work analysis(CWA)is a human factors research tool that seeks to map a system and account for users’needs,yet does not provide clear guidelines for progressing such analysis into workable designs with which users can interact.This paper seeks to present a proof-of-concept investigation to demonstrate that DwI can be suitably constrained and validated by insights gained from CWA.CWA,in turn,benefits by having a suitable toolkit for progressing insights.Two teams of individuals without design backgrounds were able to develop mock-up in-vehicle interfaces aimed at reducing fuel use.The teams were able to use DwI toolkit to articulate the genesis of their ideas,which in turn could be directly linked to system needs identified within CWA.
