Automated exercise progression in simulation-based training

As simulator-based training systems become more complex, the amount of effort required to generate, monitor, and maintain training exercises multiplies greatly. This has significantly increased the burden on the instructors, potentially making the training experience less efficient as well as less effective. Research on intelligent tutoring systems (ITS) has largely addressed this issue by replacing the instructor with a computer model of the appropriate pedagogical concepts and the domain expertise. While this approach is highly desirable, the effort required to develop and maintain an ITS can be quite significant. A more modest as well as practical alternative to an ITS is the development of intelligent computer-based tools that can support the instructors in their tasks. The advantage of this approach is that various tools can be developed to address the different aspects of the instructor's duties. Moreover, without the burden of having to replace the instructor, these tools are more easily developed and fielded in existing trainers. One aspect of an instructor's task is to assess the students' performance after each training exercise and select the next exercise based on their previous performances. It would clearly be advantageous if this exercise selection process were to be automated, thus relieving the instructor of a significant burden and allowing him to concentrate on other tasks. Therefore, the focus of this paper is the development of a stand-alone system capable of determining exercise progression and remediation automatically during a training session in a simulator-based trainer, on the basis of the students's past performance. Instructional heuristics were developed to carry out the exercise progression process. A prototype was developed and applied to gunnery training on the Army M1 main battle tank.<
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