Education 4.0: Smart Blended Learning Assisted by Artificial Intelligence, Biofeedback and Sensors

The paper presents smart blended learning for fostering students’ performance. It uses models of student activity and detects causes of a possible drop out, using learning analytics and real-time data. Taking advantage of embedded sensors (non-invasive, low-cost, flexible and distraction free) built into wearable devices real-time data can be used to support students’ success, well-being and health. In our contribution to the research topic we evaluate the students' experience with smart blended learning using biofeedback methods, sensor data extracted from wearables for five specific lectures, during study periods and in examinations. The preliminary results indicate a correlation between the physiological signals and the grades in examination.