An interactive recommender system based on reinforcement learning for improving emotional competences in educational groups

Abstract

The development of Social and Emotional competences of students can significantly improve their learning and social outcomes. This prompts for tools to assist tutors in accomplishing social and emotional learning activities and evaluating the impact achieved. To do so, the blending of Recommender Systems with Machine Learning technologies can be proven beneficial for the design of intelligent and self-learning tools with the capacity to recommend activities, aligned with the social and emotional needs of educational groups. In the current manuscript, we detail a modeling approach for an interactive Recommender System that aims to suggest educational activities to tutors for improving the social and emotional competences of students, taking advantage of Reinforcement Learning techniques. A Reinforcement Learning model has been designed that considers the evolution of students’ social and emotional characteristics and the provided feedback through a set of interactions. Short evaluation of the detailed approach is provided, focusing on validating its appropriateness to serve educational needs.