Research on Student Behavior Analysis and Grade Prediction System Based on Student Behavior Characteristics

Abstract

In the era of big data, the traditional governance model of student behavior management gradually shows the disadvantage of “post positioning”. Therefore, the research uses relevant data mining algorithms to extract and analyze students’ behavior characteristics, and constructs a SPC system model for students’ behavior analysis and performance prediction. At the same time, the experiment verifies its effectiveness. The experimental results show that in the factor analysis, the overall variance of the first seven indicators is 69.942\%, which shows that there is a significant correlation between students’ learning behavior and academic performance. In the model performance analysis of SPC system, the accuracy of the five algorithms is kept at 40% - 60%, while SVM has higher stability than other methods. In addition, the prediction accuracy and response rate of SPC model reached 86.90% and 81.57% respectively. When the sequence length was increased from 1 to 20, the accuracy of SPC model exceeded 70%; However, when the feature dimension exceeds 50, the model representation ability will decline. Therefore, appropriate feature dimensions are needed to predict student performance. To sum up, the SPC model built by the research is effective in analyzing student behavior and predicting students’ performance, and practical in actual student management.