A data mining approach for training evaluation in simulation-based training. (February 2015) - Wang, Juite; Lin, Yung-I; Hou, Shi-You
Highlights: The data mining approach can improve the effectiveness of training evaluation. Both the knowledge and confidence dimensions can influence learning performance. A real-life example is used to illustrate the proposed methodology. The ANN approach can reach more than 90% prediction accuracy. Useful rules can be discovered for improving training performance. Abstract: With the significant evolution of computer technologies, simulation has become a more realistic and effective experiential learning tool to assist in organizational training. Although simulation-based training can improve the effectiveness of training for company employees, there are still many management challenges that need to be overcome. This paper develops a hybrid framework that integrates data mining techniques with the simulation-based training to improve the effectiveness of training evaluation. The concept of confidence-based learning is applied to assess trainees' learning outcomes from the two dimensions of knowledge/skill level and confidence level. Data mining techniques are used to analyze trainees' profiles and data generated from simulation-based training for evaluating trainees' performance and their learning behaviors. The proposed methodology is illustrated with an example of a real case of simulation-based infantry marksmanship training in Taiwan. The results show that the proposed methodology can accurately evaluate trainees' performance and their learning behaviors and can discover latent knowledge for improving trainees' learning outcomes.