Enhancing online Chinese language education through intelligent computing and data clustering—A case study on personalized teaching for international students in Shanxi Province
Abstract
Providing users with high-quality personalised services in an online environment has become a research hotspot due to the rapid growth of Internet technologies. Personalized online teaching for international Chinese teachers takes domain knowledge as the core, computer and other information technologies as the support, and the network as the communication channel. It is an application system that integrates pedagogy, computer science, psychology and behavioral cognition to achieve better online teaching for international Chinese teachers. This paper focuses on the construction and analysis of student models for online teaching of international Chinese teachers, deeply studies and analyzes the advantages and disadvantages of various traditional student models, and improves the student model for online learning. The existing data mining clustering algorithms are studied and analyzed in detail to provide relevant technical support for analyzing learners’ learning characteristics. The experiment shows that the student model and M-Kmeans algorithm proposed in this paper have certain significance and potential application value in personalized online teaching for international Chinese teachers.
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DOI: https://doi.org/10.32629/jai.v7i5.1659
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