A Preliminary Study on Learners’ Personal Traits for Modelling Learner Profiles in ITS: A Sensor-free Approach

Intelligent tutoring systems (ITS) are computer-based learning systems that use intelligent technologies to provide adaptive learning paths and contents to learners by considering their characteristics and needs. The learner model, which is a representation of different learner’s characteristics, plays a key role in this adaptation. Research on educational psychology showed that emotion and learning have a hidden mutual relationship. If a learning process involves both cognitive and affective dimensions, then learning is like to be more usable, socially appropriate, acceptable and memorable, ultimately leading to significant improvement in the learner’s performance and the learning experience. However, traditional ITS ignore emotional aspects in learner modelling mostly; instead, they focus on learners’ cognition and motivation to maximise the knowledge to be achieved. The characteristics related to cognition are not well defined and primarily sparse when modelling a learner profile tailored to the learning domains. This study attempts to investigate the most common and relevant personal traits involved in the learning process based on the domains of learning. It also explores the most adopted techniques to identify such traits from log files of learners’ interactions with ITS without using physical and physiological sensors (i.e., camera, EEG) to model characteristics learner profiles for ITS aiming to improve its adaptiveness and leverage personalised learning.