Utilizing Data Mining Techniques to Analyze the Relationship Between Daily Flexibility and Performance

This study employs advanced data mining techniques to explore the relationship between daily academic flexibility and performance, with a focus on the mediating role of the classroom psychological environment. Data from 946 students at a rural boarding school were analyzed through a systematic data mining process that included data collection, preprocessing, feature selection, and model training. Key techniques used were decision trees, random forests, and logistic regression models. The preprocessing steps involved handling missing values and outliers to ensure data integrity. Feature selection was crucial in identifying the most influential variables. The analysis revealed significant positive correlations between the classroom environment, daily academic flexibility, and performance. The findings underscore the effectiveness of data mining in uncovering insights that demonstrate how a supportive classroom environment enhances performance and academic flexibility. This approach highlights the utility of sophisticated data mining methods in educational research to optimize learning environments and improve student outcomes.