Application of Data Mining Methods in Analyzing Learning Habits of Chinese Language Chinese Education Students and Improving Teaching Strategies

Abstract

This study utilizes multidimensional background information data from 280 students majoring in Chinese language education at a university. Employing data mining and K-means clustering techniques, the study conducts qualitative and quantitative statistical analysis and mining of students' learning data to gain insights into their learning motivations, explore their learning profiles and habits, and uncover the correlations between learning behaviors and academic performance. This research aims to provide theoretical support for teachers in developing teaching strategies and improving instructional practices. Analysis indicates that over 70% of students engage in outdoor activities three or more times, and those who consistently eat breakfast on time and review before class demonstrate superior academic performance. Strict attendance requirements in the classroom are correlated with final course grades. Based on these correlations, teachers can adjust their teaching strategies and methods to enhance instructional effectiveness.