Leveraging Big Data Analytics for Cultural Teaching Competence in international Chinese Linguistic Learning using Weighted Random Forest Model

Abstract

The teaching of Chinese language and culture has gained significant importance on the global stage due to China's growing influence in various domains. International Chinese language teachers play a crucial role in promoting cross-cultural understanding and facilitating effective communication between Chinese and non-Chinese speakers. This paper aims to explore the concept of cultural teaching competence for international Chinese language teachers, with a focus on the Chinese national context and the application of a cultural teaching framework supported by big data analytics. The model uses the Integrated Machine Learning Teaching Framework (iMLTF). The model constructs the cultural teaching framework for the evaluation of the International Chinese Language based on Chinese National Context. The iMLTF model uses the Multivariant examination integrated with the Weighted Random Forest model. The simulation analysis expressed that the proposed iMLTF model achieves the higher classification accuracy value of 98% compared with the conventional state-of-art techniques.