A Personalized Learning System Exploration of Artificial Intelligence in Teaching Communication Signals in Urban Rail Transit

Abstract

In order to solve the problem of unsatisfactory use of online learning system for urban rail transit communication signal course, the exploration of personalized learning system of artificial intelligence in the teaching of urban rail transit communication signal is proposed. In this paper, a personalized recommendation system is designed and implemented by combining data mining technology. The system first analyzes the user's learning behavior and dynamic features, and sets certain weights for each feature to complete the learning demand mining. Then, it matches the similarity between the learner's interest vectors and the learning resource feature vectors, and lists the optimal recommended resources after judgment. The experimental results show that 65% of the students improved their performance after learning online through the personalized recommendation system in this paper. Conclusion: The personalized learning strategy can meet the actual needs, and can effectively improve the user's learning experience and efficiency. © 2025 The Authors.

Keywords

Author Keywords

online learning Data mining dynamic features learning behavior personalized recommendation

Index Keywords

E-learning Learning systems Teaching Online learning Curricula Engineering education artificial intelligence Data mining Behavioral research Recommender systems Light rail transit Communication signals Data mining technology Dynamic features Learning behavior Online learning systems Personalized learning systems Personalized recommendation Personalized recommendation systems Urban rail transit

Author Affiliations

Guizhou Communications Polytechnic University, Guiyang, China

Funding & Acknowledgements

No funding information

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A Personalized Learning System Exploration of Artificial Intelligence in Teaching Communication Signals in Urban Rail Transit

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