Classroom Teaching Evaluation Based on Data Mining Technology

To enhance the precision of school teaching management and support teachers in enhancing their instructional practices, the author suggests implementing a teaching evaluation approach using data mining techniques. This methodology integrates contemporary data analysis technology into the educational sector to gain deeper insights into the teaching staff’s performance and the current state of teaching activities. The initial step involves gathering extensive datasets concerning both teachers and students. After collecting data, perform data cleaning to remove duplicates, missing data, and outliers, ensuring data quality and consistency. After data cleaning, the data is transformed for further analysis. Subsequently, data mining methodologies like the Apriori algorithm are employed to structure and analyze the data. The Apriori algorithm, renowned for its role in association rule mining, is a prevalent choice in this context, which can help find the correlation between teacher information, student information, and evaluation results. Finally, by applying data mining technology to classroom teaching evaluation, we can gain a deeper understanding of the educational environment, provide more effective support and services, and promote the improvement of educational quality. This method not only helps school management departments better understand teaching work, but also helps teachers continuously improve their teaching methods to better meet the needs of students.