Real-Time Cognitive Measures for Enhanced Human Performance
Closing Date: 30 November 2018
This work will focus on adaptive training, and specifically, evaluating the real-time use of cognitive measures to adjust the difficulty of immersive simulation training scenarios.
This research will enhance human performance through advanced training techniques and technologies using adaptive training. This dynamic difficulty balancing will enable trainees to receive personalised, yet cost-effective simulation-based training. At its core, adaptive training (AT) is training in which the difficulty of the problem or task is varied according to the performance of the trainee during the training exercise (Kelly, 1969). Two critical steps in achieving adaptive training relate to a) the identification and measurement of appropriate performance metrics, and b) the adaptive logic used to dynamically modify the training task. A number of AT approaches have emerged, including macro, Aptitude Treatment Interaction (ATI), micro, and two-step (Landsberg et al. 2012). These approaches involve pre-task trainee evaluation (macro/ATI), continual on-task performance (micro), or a combination of the both (two-step). Put simply, pre-task evaluation positions trainees at differing difficulty levels prior to the training task, and continual on-task evaluation adjusts the task difficulty during the training exercise. The emergence of pre-task adaptive training approaches highlights the inherent difficulty in identifying appropriate in-task performance measures. This challenge is compounded by a substantive lack of research on how to dynamically adapt a training task in response to performance. Lastly, an identified gap in the implementation of AT systems “is that there is little to no training effectiveness evaluation or assessment” (Landsberg et al. 2012, p.106).
- Evaluating trainee cognitive load as a novel performance measure for adaptive training.
- Evaluating the impact of this real-time cognitive load adaptive training approach on training performance Hypothesis
- What cognitive measures are appropriate for adaptive training in simulation-based systems?
- How can dynamic difficulty balancing, using real-time cognitive measures, be effectively implemented in an existing game-based military simulation environment?
PhD Scholarship details
Funding: $27,082 per annum (2018 rate) indexed annually. For a PhD candidate, the living allowance scholarship is for 3.5 years and the tuition fee scholarship is for four years. An additional funding to top-up the baseline stipend and to contribute to running costs is also available.
Supervisor: Dr Karen Blackmore
Available to: Australian Citizens
Applicants must be Australian Citizens and meet the eligibility criteria for admission.
Interested applicants should send an email expressing their interest along with scanned copies of their academic transcripts, CV, a brief statement of their research interests and a proposal that specifically links them to the research project.
Please send the email expressing interest to Karen.Blackmore@newcastle.edu.au by 5pm on 30 November 2018.
Applications Close 30 November 2018
|Contact||Dr Karen Blackmore|
|Phone||+61 2 4921 5206|
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