Using after-action review based on automated performance assessment to enhance training effectiveness.
Training simulators have become increasingly popular tools for instructing humans on performance in complex environments. However, the question of how to provide individualized and scenario-specific assessment and feedback to students remains largely an open question. In this work, we follow-up on previous evaluations of the Automated Expert Modeling and Automated Student Evaluation (AEMASE) system, which automatically assesses student performance based on observed examples of good and bad performance in a given domain. The current study provides a rigorous empirical evaluation of the enhanced training effectiveness achievable with this technology. In particular, we found that students given feedback via the AEMASE-based debrief tool performed significantly better than students given only instructor feedback on two out of three domain-specific performance metrics.
- Research Organization:
- Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 1026982
- Report Number(s):
- SAND2010-6514C; TRN: US201121%%193
- Resource Relation:
- Conference: Proposed for presentation at the Human Factors and Ergonomics Society Meetings held September 27-October 1, 2010 in San Francisco, CA.
- Country of Publication:
- United States
- Language:
- English
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