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RLTutor: Reinforcement Learning Based Adaptive Tutoring System by Modeling Virtual Student with Fewer Interactions

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arxiv 2108.00268 v1 pith:VSXUQUTK submitted 2021-07-31 cs.AI cs.CYcs.LG

classification cs.AIcs.CYcs.LG
keywords learningstudentsactualmathematicalmodelreinforcementstudentteaching
verification ladder T0 review T1 audit T2 compute T3 formal
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A major challenge in the field of education is providing review schedules that present learned items at appropriate intervals to each student so that memory is retained over time. In recent years, attempts have been made to formulate item reviews as sequential decision-making problems to realize adaptive instruction based on the knowledge state of students. It has been reported previously that reinforcement learning can help realize mathematical models of students learning strategies to maintain a high memory rate. However, optimization using reinforcement learning requires a large number of interactions, and thus it cannot be applied directly to actual students. In this study, we propose a framework for optimizing teaching strategies by constructing a virtual model of the student while minimizing the interaction with the actual teaching target. In addition, we conducted an experiment considering actual instructions using the mathematical model and confirmed that the model performance is comparable to that of conventional teaching methods. Our framework can directly substitute mathematical models used in experiments with human students, and our results can serve as a buffer between theoretical instructional optimization and practical applications in e-learning systems.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Constructing a Question-Answering Simulator through the Distillation of LLMs

    cs.LG 2025-09 conditional novelty 6.0 of 10

    LDSim distills an LLM's concept-prerequisite knowledge and mastery reasoning into a lightweight simulator that beats LLM-based and LLM-free baselines on four knowledge-tracing datasets.

  2. Personalized Education with Ranking Alignment Recommendation

    cs.AI 2025-07 conditional novelty 6.0 of 10

    Ranking Alignment Recommendation adds a collaborative ranking loss to RL-based question recommenders, improving simulated learning effects across five environments.

  3. GraphRAG-Induced Dual Knowledge Structure Graphs for Personalized Learning Path Recommendation

    cs.IR 2025-06 conditional novelty 6.0 of 10

    KnowLP combines LLM-generated prerequisite and similarity knowledge graphs with reinforcement learning to recommend personalized learning paths, reporting state-of-the-art results on Junyi, MOOCCubeX, and ASSISTments2009.

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