  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.
