Ontology-driven Reinforcement Learning for Personalized Student Support
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929487099199488 |
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| author | Hare, Ryan Tang, Ying |
| author_facet | Hare, Ryan Tang, Ying |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_10332 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Ontology-driven Reinforcement Learning for Personalized Student Support Hare, Ryan Tang, Ying Computers and Society Machine Learning Multiagent Systems 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. |
| title | Ontology-driven Reinforcement Learning for Personalized Student Support |
| topic | Computers and Society Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2407.10332 |