Process Eye-Tracking Data with Machine Learning Approach in Context of Fundamentals of Electrical Engineering

Abstract

In this research study, first-semester students solved tasks in the context of the fundamentals of electrical engineering. They were wearing eye-trackers while solving the tasks. The collected data is used to train a machine learning model per task, that predicts based just on eye-tracking data, if a student is about to succeed in solving a specific task or if the student is about to fail. The trained models reach an accuracy of 85% respectively 91% depending on the task. In the future, this model will be integrated into a virtual environment where eye-trackers are present, to assist those students who might fail.