Multi-modalities Analysis In Profiled Learning

Abstract

In the context of the modern development of informational technologies, there is a great impact on education in all aspects such as variety and quality. Moreover, student center approaches become the main goal of many institutions all over the world and this can lead us to Adaptive Learning. Adaptive learning is mainly focusing on enhancing the student whether in the process of learning or even in the assessment. This project aims to take some di erent modalities from the learner and predict his/her result in the exam. We performed an experiment research in which 53 students, ranging in age from 18 to 22, solved an English exam. The study explored the possibility of adding several student modalities such as eye gazing, facial expressions, and mouse movements. The analysis of the collected dataset shows that adding student features can e ectively predict his assessment score. We used di erent regression models to predict the score of the student based on his features.