Multi-agent system model for tutor recommendation in ubiquitous learning environments

Ubiquitous learning environments allow to learn anywhere at anytime, enabling people to have better learning experiences in their daily lives. In order to detect learning problems and offer help, a tutor needs to observe the actions of students and to evaluate them, which is not easy to accomplish in a ubiquitous environment. Therefore, this work presents a multi-agent model to generate recommendations of tutors in the topic that a student needs help with, based on the experiences of these tutors with other students, their availability and their physical proximity. The proposed model allows to monitor the student interaction within the learning environment, detect learning problems and offer personalized help.