Research on Application of Online Teaching Performance Prediction Based on Data Mining Algorithm

The application of data mining in teaching has entered the stage of development. During the epidemic, colleges and universities have accumulated a large amount of online teaching statistical data. These data can be used to establish a classification model for predicting student performance. In this paper, the Naive Bayes algorithm is improved to solve the problem of underflow when the data feature values are too large. The student performance prediction classification model is constructed, and the classification efficiency and accuracy are improved to a certain extent. The improved model is used to predict and warn the performance in the mid-term stage to prevent The phenomenon of a large proportion of missing subjects, thereby ensuring the quality of students' learning throughout the semester.