Prediction of student attendance and its scheduling optimization in higher education based on multivariate regression analysis

In order to improve the attendance rate of students and optimize the quality of teaching, this paper proposes a method of predicting the attendance rate of students in colleges and universities based on multivariate regression analysis.Firstly, we obtain the factors affecting students' attendance rate through sample survey and conduct correlation analysis, and then summarize and refine the three dimensions of students, teachers and schools.The above dimensions are used as independent variables to construct regression equations, and the regression equations are used to predict the attendance rate of students, so that teaching managers can optimize the management.The analysis found that the factors such as the college to which the truant students belonged, the reason for truancy, and the grade level showed diversity and complexity.Overall male students have more truancy rates than female students, and lecturers with higher titles have lower truancy rates.Regression modeling and prediction of truancy rate found that the prediction results are closer to the real results.Therefore, the method of this paper can be combined to optimize and adjust the attendance rate from the aspects of regulations, work allocation, teaching management and ideological education.