PERFORMANCE EVALUATION of MILITARY TRAINING EXERCISES USING DATA MINING

Abstract: Attaining training objectives is the measure of a successful training as objectives defines the purpose of instructional events. Application of the training objectives is challenging in large and complex military trainings. The trainings in military domain not only focus on the completion of the trainings but effectively achieving the objectives of the training is the goal of the exercises. It has been realized that the performance to achieve the goal is strengthen by the instructional processes and materials which are crafted to address specific training objectives. Simulation is one of the effective and realistic learning tools which can be used in trainings. As it is known that simulation generates enormous data, analysis of this data which may contain hidden information is a challenging task. The use of data mining is a solution to this problem. The aim of this project is to propose a framework of a system for the instructors which can be followed for evaluating trainee’s performance so that their fulfillment of the training objectives can be improved. A proposal which is studied in this project is learning from previous training experiences using data mining techniques to improve the effectiveness of the training by predicting the performance of the trainee. For selecting the good prediction model to estimate the learning outcome of the trainees, different classification techniques have been compared. CRISP-DM model is considered as a base for proposing the framework in this dissertation. Proposed framework is then applied on the dataset obtained from the Swedish Military for the exercises which involved shooting the target.