Learning Management System (LMS) Design Toward Precision Education Using Brain Data

The integration of computational analysis models in education is becoming increasingly significant, providing insights into student behavior and performance. This study presents the design and implementation of a learning management platform that utilizes signal analysis and machine learning techniques on neuroeducation data. By collecting and analyzing electroencephalogram (EEG) data from students engaged in educational activities on Moodle, the platform offers data-driven insights for improving learning outcomes. The study involved EEG data collection from 21 students using the Muse 2 headset while they participated in structured activities, including self-assessment tests, video learning, and interactive exercises. The findings suggest that EEG-based computational analysis can enhance precision education by identifying cognitive engagement patterns, informing adaptive learning strategies, and optimizing resource allocation.