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.
Code (0)
등록된 구현이 없습니다.
Tasks
Multi-agent Reinforcement Learningreinforcement-learningReinforcement LearningSimilar Papers 제목 키워드 기반
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a key tool to reduce the development cost and …
Deep Reinforcement LearningExplainable artificial intelligenceMathreinforcement-learning+1Advanced Mathematics Learning Behavior Prediction and Academic Early Warning Model Based on Multimodal Data Analysis
Early detection of at-risk students and timely academic intervention pose major challenges in advanced mathematics education, where complex conceptual hierarchies and nonlinear learning trajectories often hold back stude…
Iris: An AI-Driven Virtual Tutor For Computer Science Education
Integrating AI-driven tools in higher education is an emerging area with transformative potential. This paper introduces Iris, a chat-based virtual tutor integrated into the interactive learning platform Artemis that off…
Few-Shot LearningUsing AI in engineering education: a balancing act, driven by clear purpose
Based on a questionnaire of 100 higher-education students, predominantly from engineering-related fields, and a critical review of recent literature, this chapter examines how students use and perceive Large Language Mod…
Ontology-driven personalized information retrieval for XML documents
This paper addresses the challenge of improving information retrieval from semi-structured eXtensible Markup Language (XML) documents. Traditional information retrieval systems (IRS) often overlook user-specific needs an…
Information RetrievalSemantic Similarity