Ontology-driven Reinforcement Learning for Personalized Student Support

• URL: http://arxiv.org/abs/2407.10332v2
• Date: Thu, 5 Sep 2024 13:16:07 GMT
• Title: Ontology-driven Reinforcement Learning for Personalized Student Support
• Authors: Ryan Hare, Ying Tang,
• Abstract summary: This paper presents a general-purpose framework for personalized student support, applicable to any virtual educational system. We apply 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.
• Score: 1.8972913066829966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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