C4.5: programs for machine learning
C4.5: programs for machine learning
Motion Tracking: No Silver Bullet, but a Respectable Arsenal
IEEE Computer Graphics and Applications
Cognitive Computer Tutors: Solving the Two-Sigma Problem
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Neural Networks
Experimental Assessment of Accuracy of Automated Knowledge Capture
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Physiological-Based Assessment of the Resilience of Training to Stressful Conditions
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Automatic system for the detection and analysis of errors to support the personalized feedback
Expert Systems with Applications: An International Journal
Communications-based automated assessment of team cognitive performance
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
Today's competitive objective: augmenting human performance
FAC'11 Proceedings of the 6th international conference on Foundations of augmented cognition: directing the future of adaptive systems
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This paper presents automated expert modeling for automated student evaluation, or AEMASE (pronounced “amaze”). This technique grades students by comparing their actions to a model of expert behavior. The expert model is constructed with machine learning techniques, avoiding the costly and time-consuming process of manual knowledge elicitation and expert system implementation. A brief summary of after action review (AAR) and intelligent tutoring systems (ITS) provides background for a prototype AAR application with a learning expert model. A validation experiment confirms that the prototype accurately grades student behavior on a tactical aircraft maneuver application. Finally, several topics for further research are proposed.