Tailoring Training for Disparately Skilled Participants in Large Scale Training Exercises

Large scale training exercises involve many trainees at various stages of their training maturity and at various levels of skill. Problems arise in large scale exercises when less mature or lower skilled trainees are exposed to training scenarios that are too advanced or too complex for their level of training maturity. These trainees are more likely to fail the mission they are given in the training scenario, thus reducing the benefits of training and leading to frustration in the trainee. We propose to develop a tool called SKATE (Skill Appropriate Training Environment) that will quantify skill levels of trainees, teams, or units, generate skill appropriate training objectives, modulate the difficulty of training scenarios, and provide a continuous skill-level assessment and scenario adaptation of disparately skilled trainees, teams, and units in a large scale training exercise while maintaining the overall integrity and realism of the mission itself. SKATE will evaluate the skill level of an individual, team, or unit level participants and generate a list of skill-appropriate training scenario components that can be configured into the exercise so as to fulfill the training objectives of the participants. SKATE will be designed on the basis of relational data-mining technology.