Research on the Optimization Path of Multi-scene Teaching Strategies for English in Higher Vocational Railway Industry Based on Reinforcement Learning

The international development of the railroad industry puts forward higher requirements for the English application ability of senior railroad students, and reinforcement learning provides new ideas for the optimization of their teaching strategies.Based on reinforcement learning, the article constructs an adaptive learning path recommendation model (RL4ALPR).The model achieves application learning of multi-scenario knowledge of English in the railroad industry through railroad English knowledge level modeling, candidate learning item screening, recommender modeling, and reward calculation.The recommended effective value of the model in this paper is 0.581 at a learning path length of 60, which is 7.79% to 13.70% higher than the control model.The model realizes accurate recommendation of English exercises for the railroad industry based on the answers to the exercises.The evaluation scores of the students in the experimental class under the intervention of the model in this paper are improved to 24.26, 17.50, and 19.64 for speaking, reading comprehension, and translation of English in the railroad, respectively.Under the model of this paper, English teaching in the higher vocational railroad industry is highly recognized by students in terms of "content setting", "teaching quality" and "teaching effect".And the experimental class is better than the control class in terms of the level of knowledge about English for the railroad industry, the application of English for the railroad industry in multiple scenarios, and the comprehensive ability evaluation scores of 4-5 points more than the control class.