Uncovering Learning Styles through Eye Tracking and Artificial Intelligence

Most recently, the number of students dropping out of universities or higher education institutions increased dramatically. This might be partly because of the students' limited capability exploring their own learning paths within a certain course. The introduction of adaptive learning management systems could be a potential solution to this issue. Based on individual's learning styles, these systems recommend customised and tailored learning paths. These learning styles are commonly identified using questionnaires and learning analytics, but both methods are prone to errors. While questionnaires potentially give superficial answers due to e.g. time constraints, Learning Analytics cannot mirror offline behaviour. This paper proposes an alternative to classify the learning style of individuals through the integration and combination of eye tracking and artificial intelligence algorithms. The eye movement data, which is collected in a study including more than 100 participants, is processed with different methodolgies such as data scaling and subsequently classified using various models ranging from Logistic Regression to Neural Network. Moreover, this experiment setting discovers the interplay between preprocessing and classification techniques based on complex eye tracking metrics in order to determine the most promising solutions for learning style identification. Ultimately, this comprehensive analysis not only enables the understanding of individuals' subconscious processes, but could also lead to improved educational outcomes.