Data mining method for monitoring students' distance learning behaviour based on decision tree

Because the traditional data mining methods for monitoring students' distance learning behaviours have low mining accuracy and recall, a data mining method for monitoring students' distance learning behaviours based on decision trees is proposed. First, the collected surveillance video image is compressed by OpenCV technology, and then transmitted to the client through UDP transmission mode. Then, the frequency domain enhancement method is used to enhance the processing of the surveillance image. The attention-based hybrid encoder decoder model is used to extract the sequence characteristics of students' remote learning behaviour from the enhanced surveillance image. Finally, the improved C4.5 algorithm is used to construct a decision tree, realise data mining for monitoring students' remote learning behaviour. The experimental results show that the mining accuracy and mining recall rate of this method reach more than 99%, which can accurately mine the monitoring data of students' distance learning behaviour.