Title :
Quality of Training Effectiveness Assessment (QTEA); A neurophysiologically based method to enhance flight training
Author :
Schnell, Tom ; Keller, Mike ; Poolman, Pieter
Author_Institution :
Operator Performance Lab., Iowa City, IA
Abstract :
Today, flight trainers use objective measures of task performance and additional estimated, subjective data to assess the cognitive workload and situation awareness of trainees. This data is very useful in training assessment but trainees can succeed at performing a task purely by accident (referred to as ldquomiserable successrdquo). Additionally the trainee can be in a less than optimal for learning cognitive state when the instructor operator applies brute force training tasks and methods with little regard to the learning curve which can result in the training being too easy or more often too difficult thereby inducing negative learning. In order to provide the instructor with additional quantitative data on student performance, we have designed the quality of training effectiveness assessment (QTEA) concept. QTEA is conceived as a system that allows the trainer to assess a student in real-time using sensors that can quantify the cognitive and physiological workload. Patterns of cognitive and physiological behavior can be established for experts to provide a learning benchmark towards which new trainees can be trained. Using QTEA, the trainer can quantify the studentpsilas workload level in real-time so that the scenarios can be adjusted to an optimal intensity. The cognitive and physiological measures also serve as a quantitative manifestation of a studentpsilas learning curve and it will be possible for the trainer to detect plateaus in learning. QTEA is under development but once fielded as an operational system, the trainer will be able to assess the needs for further training in a student. The basic idea of QTEA is to give the trainer a real-time picture of the performance of a trainee based on human physiological and cognitive data, flight technical, and mission specific data. In this paper, we are presenting the QTEA framework in the context of an ongoing project that studies trainee performance in a simulated Urban Close-Air Support Task.
Keywords :
aerospace computing; aerospace simulation; cognitive systems; learning (artificial intelligence); cognitive workload; flight training; learning cognitive; negative learning; neurophysiologically based method; operational system; quality of training effectiveness assessment; situation awareness; urban close-air support task; Accidents; Aerospace electronics; Aerospace simulation; Cats; Cities and towns; Laboratories; Medical diagnostic imaging; Real time systems; Sensor systems; Testing;
Conference_Titel :
Digital Avionics Systems Conference, 2008. DASC 2008. IEEE/AIAA 27th
Conference_Location :
St. Paul, MN
Print_ISBN :
978-1-4244-2207-4
Electronic_ISBN :
978-1-4244-2208-1
DOI :
10.1109/DASC.2008.4702840