Mining Social and Affective Data for Recommendation of Student Tutors

This paper presents a learning environment where a mining algorithm is used to learn patterns of interaction with the user and to represent these patterns in a scheme called item descriptors. The learning environment keeps theoretical information about subjects, as well as tools and exercises where the student can put into practice the knowledge gained. One of the main purposes of the project is to stimulate collaborative learning through the interaction of students with different levels of knowledge. The students' actions, as well as their interactions, are monitored by the system and used to find patterns that can guide the search for students that may play the role of a tutor. Such patterns are found with a particular learning algorithm and represented in item descriptors. The paper presents the educational environment, the representation mechanism and learning algorithm used to mine social-affective data in order to create a recommendation model of tutors.