Automated Expert Modeling for Automated Student Evaluation

  • Robert G. Abbott
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4053)


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.


Receiver Operating Characteristic Curve Machine Learning Technique Student Evaluation Intelligent Tutor System Subject Matter Expert 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Robert G. Abbott
    • 1
  1. 1.Sandia National Labs MS 1188AlbuquerqueUSA

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