The Optimization and Innovation of Online Practical Teaching Systems in Tourism Management Majors Under Intelligent LGBCAN Model

Abstract

This study addresses issues of redundant student interaction paths and imprecise teaching recommendations in online practical teaching for tourism management programs. To overcome these challenges, the study introduces the Light Gradient Boosting Machine-Controller Area Network model. This model helps establish an optimized online teaching system. Without altering the original teaching content, this model intelligently allocates teaching tasks and schedules system resources. The study involved 432 undergraduate and graduate students in tourism management and conducted a four-stage comparative test, including phases before model implementation and after one, two, and three months of use. The testing methodology combined task scenario–driven approaches, data log tracking, and subjective feedback analysis. In subjective feedback, overall satisfaction among undergraduates without practical training experience increased by 51.9%.

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