Abstract
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.
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Abbott, R.G. (2006). Automated Expert Modeling for Automated Student Evaluation. In: Ikeda, M., Ashley, K.D., Chan, TW. (eds) Intelligent Tutoring Systems. ITS 2006. Lecture Notes in Computer Science, vol 4053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11774303_1
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DOI: https://doi.org/10.1007/11774303_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35159-7
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