Ontology-driven Reinforcement Learning for Personalized Student Support
<|reference_start|>Ontology-driven Reinforcement Learning for Personalized Student Support: 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.<|reference_end|>
@article{hare2024ontology-driven,
    title={Ontology-driven Reinforcement Learning for Personalized Student Support},
    author={Ryan Hare and Ying Tang},
    journal={arXiv preprint arXiv:2407.10332},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10332},
    primaryClass={cs.CY cs.LG cs.MA}
}