Brain Cognitive Performance Identification for Student Learning in Classroom

Abstract

Human has sustainability to concentrate about 45-50 minutes, approximately. The student who spent a long time during the class without a break is decreasing the brain learning ability. Taking mental breaks every 45 minutes is considered as stress reduction and prepared for better learning. However, a person has a different level to maintain a focus on learning, which is longer or shorter. The well-established information for manipulating this problem is necessary to support the instruction and teaching planning. Therefore, this study proposes the method to define the learning state of each student via brain cognitive performance identification and information technology innovations. The brain signals of students are recorded by electroencephalography (EEG) during studying. Due to the performance values are presented under the specific neuroscience criteria, the Decision Tree algorithm is chosen to perform learning state classification and description. The results present the several levels of cognitive performance including low, neutral, good, and high level, which is related to the learning ability of a student. The student who has low cognitive performance will be noticed to have a mental break before class ends appropriately. The classification method provides 87% of accuracy, which is acceptable to support the implementation of the decision tree with neuroscience in this study.