Research on Data Mining Combination Model Analysis and Performance Prediction Based on Students’ Behavior Characteristics

Abstract

Using data mining technology to analyze students’ behavior can effectively predict students’ performance and other evaluation indicators, which is of great significance to improving the level of school information management. Aiming at the problems of imperfect information management platforms and low data analysis ability in colleges and universities, a data mining algorithm based on students’ behavior characteristics is proposed. Firstly, the characteristics of the GBDT algorithm, the ANN algorithm, and the K-means algorithm are analyzed, and the three algorithms are combined to establish a combined prediction model. At the same time, five standard data sets are combined for simulation training. Then, an analysis and prediction platform based on the characteristics of students’ behavior is built. Combined with the management systems such as “Campus All-in-one Card,” educational administration, and library, the data collection, modeling, analysis, and mining are realized, and the evaluation index system of students’ behavior is established. Finally, the data of students’ consumption laws, living habits, learning, and Internet access are used to verify the combined model. The results show that compared with a single algorithm, the combined model has fast run speed and high accuracy, and the prediction results are consistent with the actual situation. The prediction platform can analyze the characteristics and laws of student behavior data, effectively predict the learning effects, grasp the dynamics of students’ lives and learning in real time, and provide decision-making for school teaching management and teachers’ teaching reform.