Ontology-driven Reinforcement Learning for Personalized Student Support

TL;DR

This paper introduces an ontology-driven reinforcement learning framework that personalizes student support in virtual educational systems, enhancing adaptability and efficiency in educational assistance.

Contribution

It presents a novel modular framework combining ontologies and multi-agent reinforcement learning for personalized student support across various virtual educational platforms.

Findings

Framework adaptable to any virtual educational system

Effective personalization of student assistance demonstrated

Combines semantic organization with reinforcement learning

Abstract

In the search for more effective education, there is a widespread effort to develop better approaches to personalize student education. Unassisted, educators often do not have time or resources to personally support every student in a given classroom. Motivated by this issue, and by recent advancements in artificial intelligence, this paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system such as a serious game or an intelligent tutoring system. To fit any educational situation, we apply ontologies for their semantic organization, combining them with data collection considerations and multi-agent reinforcement learning. The result is a modular system that can be adapted to any virtual educational software to provide useful personalized assistance to students.