Adaptive learning path planning system based on reinforcement learning

Abstract

The invention relates to an adaptive learning path planning system based on reinforcement learning. The system comprises three modules of an environmental simulation module, a strategy training moduleand a path planning module. In the whole process, the ability value of the student at each moment is obtained according to an improved project reflection principle. Based on a Markov decision process, a complex learning environment is simulated, a reinforcement learning algorithm is reasonably applied to be combined with a historical learning trajectory offline training path planning strategy ofstudents, and finally, a learning path is adaptively planned for the students online according to the trained strategy. Finally, based on the idea of reinforcement learning, a complex scene learned onan online education platform is constructed in a framework of a Markov decision process, and continuous recommendation of learning resources is provided for students and an optimal learning path is planned by taking efficient acquisition capability improvement as a target, so that the learning effect and the learning efficiency of the learners are improved.

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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