Automated expert modeling for automated student evaluation

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.