Simulation Training in Self-Regulated Learning: Investigating the Effects of Dual Feedback on Dynamic Decision-Making Tasks

Abstract

Self-Regulated Learning (SRL) is a popular concept in the research area of the education. However, most researchers who have studied SRL focus on the theoretical aspects of metacognition or the educational application such as children’s learning and academic performance. The purpose of this research is to investigate the SRL effects of dual feedback (retrospective confident judgments and task performances) in a dynamic task environment. A human-in-the-loop simulation experiment was conducted to collect real-time task performance data from participants and compared the self-regulated learning effects between different feedback conditions. We found that an improvement in the accuracy of their performance prediction might promote an increase in their situation awareness on dynamic decision-making tasks. This research will contribute design faster and more effective training algorism to inexperienced operators in the computer simulation training environment.