Effective Method for Identifying Student Learning Ability During Classroom Focused on Cognitive Performance

Abstract

Cognitive performance is associated with learning ability and academic success of a student. Since an individual student may lose attention during class, a study on cognitive performance and focus sustainability can improve their academic performance. Moreover, cognitive performance is influenced by various factors called cognitive factors including gender, body mass index, and alcohol drinking state. These factors encourage performance to improve or decline eSense meters, attention, and relaxation. Therefore, this study proposes an effective method to identify brain cognitive performance of a student by using a decision tree algorithm with relative scale and cognitive factors. Brainwaves of students are recorded by electroencephalography during 45 min class with separated four periods of detection, 10 min per period. The detected data are calculated to cognitive performance value via relative scale equation. It is compiled into a decision tree with mentioned factors for accurate classification. The accuracy results of the decision tree model for four periods of cognitive performance are 100%, 100%, 94%, and 98%; which are strongly acceptable. In addition, there are different levels of cognitive performance including low, neutral, good, and high. It indicates that some students have a lower level of performance during the class. In conclusion, the proposed method can identify cognitive performance level of individual students accurately. Classified results can improve learning performance of student along with enhancing class management.