Publications
Enhanced training effectiveness using automated student assessment
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 an 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.