Advanced Learner Assistance System's (ALAS) Recent Results

This work presents a real-time biofeedback tool that employs wearables and the Internet of Things with educational applications to improve students' learning and retention. We aimed to create a web platform using the Internet of Things (IoT) and Machine Learning (ML) architecture to predict students' performance, analyze mental fatigue, and provide real-time quantitative biofeedback to identify the best learning modality. Thus, the main goal was to develop a system that allows students to learn and improve their projects. We integrated the analysis of real-time biometric signals, machine learning algorithms, and web services as we observed their behavior under different learning modalities, seeking to improve cognitive performance. For this, 23 volunteers filled out the ten-question Fatigue Assessment Scale questionnaire about mental fatigue, validated with the P300 waves acquired during auditory-oddball (AO) tests. Synchronized data acquisition was achieved using Enophones and an E4 wristband. To develop predictive models, we collected the biometric data and incorporated it into an ML algorithm to visualize students' performance in real time. The system can accommodate other wearable systems with new features in further experiments. Thus, we believe this current development has the potential to further revolutionize traditional teaching with this methodology and future enhancements.