Early warning and risk assessment algorithm of school discipline inspection and supervision cases based on data mining technology

Abstract

At present, the discipline inspection and supervision work of schools is faced with the problems of large amount of data and complex information, and efficient algorithms are urgently needed for accurate early warning and risk assessment. The purpose of this study is to build an efficient and accurate case early warning and risk assessment system through data mining technology. In the research, we adopted an ensemble learning algorithm, combining the advantages of deep learning and traditional machine learning, to conduct an in-depth analysis of a large number of historical case data in the field of education. The experimental results show that the early warning accuracy rate of the system on the test data set reaches 85%, which is 45 percentage points higher than that of the traditional method, effectively reducing the false report and false alarm rate of discipline inspection and supervision work, and significantly improving the work efficiency. Through this study, we not only verify application potential of data mining technology in field of educational discipline inspection and supervision but also provide scientific decision support tools for schools, which are helpful to prevent potential violations and maintain educational equity.