An Improved Online Teaching Method for Chinese Language Courses in Higher Vocational Education based on Hybrid Machine Learning

The dynamic growth of online education in higher vocational learning requires the construction of intelligent systems that can evaluate and refine the quality of teaching, particularly in language education such as Chinese. Traditional methods to examine online teaching typically lack the ability to deliver concise and scalable outcomes due to their limited flexibility and poor classification performance. To address the aforementioned challenges, this study proposes a hybrid Machine Learning (ML) framework that combines Random Forest (RF) for feature selection with Support Vector Machine (SVM) for classification, aiming to improve the evaluation and instructional support of online Chinese language teaching. The model has been developed to examine instructional behaviors, performance trends, and teaching dimensions to classify teaching effectiveness effectively. Empirical findings demonstrate that the suggested hybrid model attains an impressive classification accuracy of 98.9% and therefore validates its high predictive power. Additional evaluation metrics further affirm its performance, with a precision of 98.4%, recall of 98.6%, F1-score of 98.5%, and specificity of 97.8%. The model exhibits low error rates with high flexibility which makes it suitable for real-time learning platforms. These findings highlight the practical potential of hybrid learning models for optimizing teaching practice and facilitating data-informed decision-making in vocational language learning, a significant advancement over traditional assessment systems.