Automatic Cognitive Workload Classification Using Biosignals for Distance Learning Applications

Current e-learning platforms provide recommendations by applying Artificial Intelligence algorithms to model users’ preferences based on content, by collaborative filtering, or both, thus, do not consider users’ states, such as boredom. Biosignals and Human-Computer Interaction will be used in this study to objectively assess the state of the user during a learning task. Preliminary data was obtained from a small sample of young adults using physiological sensors (e.g., electroencephalogram, EEG, and functional near infrared spectroscopy, fNIRS) and computer interfaces (e.g., mouse and keyboard) during cognitive tasks and a Python tutorial. Using Machine Learning (ML), Cognitive Workload was classified considering EEG and fNIRS. The results show that it is possible to automatically distinguish cognitive states with accuracy around 84%. This procedure will be applied to adjust the difficulty level of learning tasks, model user preferences, and ultimately optimize the distance learning process in real-time, in a future e-learning platform.