A self-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 an environmental simulation module, a strategy training module and a path planning module. In the whole process, the ability value of the student at each moment is obtained according to the 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 student historical learning track offline training path planning strategy, and finally, alearning path is adaptively planned for the student online according to the trained strategy. Finally, based on the idea of reinforcement learning, the complex scene learned on the online education platform is constructed in the framework of the Markov decision process, the purpose of improving the efficient obtaining capability is achieved, continuous recommendation of learning resources is provided for students, the optimal learning path is planned, and therefore the learning effect and learning efficiency of the learner 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 l

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