Analysis Model of Students Learning Behavior Based on Extreme Random Forest Classification Algorithm

Abstract

With the continuous development of information technology, under the existing teaching environment and the degree of informationization, relying only on teachers to pay attention to each student’s learning situation is not able to accurately analyze the students' status. To solve this issue, the study suggests a hierarchical multilabel classification technique based on clustering relationships. Using the integration principle, the clustered clustering tree is built as a random forest classification model after the labels in the sample set are clustered using the K-mean clustering method. Moreover, the random forest classification model is improved to address the problem of reduced classification effect caused by the increased randomness of the decision tree. The outcomes indicated that the accuracy of the multilabel classification algorithm with clustered relationships was 95.6%, the false alarm rate was 2.3%, the F1 value was 0.97, and the root-mean-square error value was 0.12 at a training set size of 800. The accuracy of the random forest algorithm, the K-nearest neighbor algorithm, and the adaptive extreme random forest classification algorithm models were 0.49, 0.61, and 0.75 at a training set size of 1000, respectively. At a validation set size of 500, the accuracy of the three algorithm models was 0.41, 0.47, and 0.64, respectively. The outcomes demonstrated that the proposed improved algorithmic model has a better performance compared to other methods, which can improve the level of education informatization and promote the intelligent development of education and teaching.