Publications
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. © Springer-Verlag Berlin Heidelberg 2006.