Intelligent tutoring system to improve learning outcomes

Abstract

Nowadays, society is in constant evolution, which allows constant production of new knowledge. In this way, citizens are constantly pressured to obtain new qualifications through training/requalification. The need for qualified people has been growing exponentially, which means that resources for education/training are limited to being used more efficiently. In this paper we will focus in the design the user model, so, we propose an innovative approach to design a user model that monitors the user's biometric behaviour by measuring their level of attention during e-learning activities. In addition, a machine learning catego-rization model is presented that oversees user activity during the session. We intend to use non-invasive methods of intelligent tutoring systems, observing the interaction of users during the session. Furthermore, this article highlights the main biometric behavioural variations for each activity and bases the set of attributes relevant to the development of machine learning classifiers to predict users' learning preference. The results show that there are still mechanisms that can be explored and improved to better understand the complex relationship between human behaviour, attention and evaluation that could be used to implement better learning strategies. These results can be decisive in improving ITS in e-learning environments and to predict user behaviour based on their interaction with technology devices.