Employing Multi-Sensors to Implement Real-Time Neurofeedback System for Improving Performance of STEM Curriculum

Abstract

This study aims based on brainwave and visual sensors to construct an attention recognition mechanism and apply it to develop a real-time neurofeedback system that can maintain an effective learning status. Through employing attention recognition mechanism, this system can activate a feedback to improve his/her attention in real-time when learners' reading attention was detected too low. In this investigate, a total of 30 students from one high school in South Taiwan were recruited to participate in this experiment. Science, technology, engineering and mathematics (STEM) education issue have received a lot of attention. Researchers, educators, practitioners, and business communities forwarded several high profile proposals to improve competitiveness in science and technology development (Kuenzi, 2008). On the other hand, learning is an active processes of cognition, especially (Freeman et al., 2014). Schneps, Thomson, Chen, Sonnert, and Pomplun (2013) noted that sustain learner's attention is a very important in learning activities. According to attention can increase memory, comprehension, and cognition, which further improving learning performance. Hence, we develop a real-time neurofeedback system to enhance learner's attention in reading e-book. In this study, participants had asked to wear NeuroSky to reading e-books during experimental progress. The brainwave functions automatically activated to detect learner's attention. The briefly data had collected and analysis. The results indicate the neurofeedback can provide a way of feedback to affect learner's brain state to maintain an effective learning status, and further improving their learning performance. In the future, we suggest that designing further experiments to verify or enhance the principles and cues adopted. Different backgrounds variable can use to divide into difference group for investigating the influence of learning.