An Adaptive Learning Path Planning System Based on Reinforcement Learning

Abstract

The invention relates to an adaptive learning path planning system based on reinforcement learning, including three modules of environment simulation, strategy training and path planning. The ability value of students at each moment obtained according to the improved project reflection principle in the whole process is based on Marr The Kove decision-making process simulates the complex learning environment, and reasonably applies the reinforcement learning algorithm combined with the student's historical learning trajectory to train the path planning strategy offline, and finally plans the learning path for the student online adaptively according to the trained strategy. Finally, based on the idea of ​​reinforcement learning, the present invention constructs the complex scenes learned on the online education platform in the framework of the Markov decision-making process, aims at improving the ability to obtain high-efficiency learning resources, and provides students with continuous recommendation of learning resources and optimal planning learning path, so as to improve the learning effect and learning efficiency of learners.

Description

technical field [0001] The invention relates to an adaptive learning path planning system based on reinforcement learning, which belongs to the technical field of computer applications. Background technique [0002] With the increasing popularity of online education, students can use a variety of e-learning resources, including e-books, after-school exercises and learning videos. In view of the diversity and differences of students' backgrounds, learning styles and knowledge levels, online education platforms need to introduce Personalized learning resource recommendation tool to facilitate students to choose their own learning path and meet their individual learning needs. [0003] The existing personalized learning resource recommendation algorithms can basically be divided into two categories, rule-based recommendation and data-driven recommendation. Most intelligent tutoring systems (Intelligent Tutoring System, ITS) mostly use rule-based methods for learning The recomm...

AI Technical Summary

Problems solved by technology

[0004] Although the data-driven recommendation method is more scalable and versatile than the rule-based method, the existing solutions all have the same problem in realizing the adaptive learning resource recommendation for students, that is, they can only be based on The content of learning resources or the learning behavior of students is used to search for learning resources with similar content or among students with similar learning behaviors, without considering the difficulty of learning resources and the impact of dynamic changes in students' learning status

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