Student Behavior Based on Machine Learning Algorithms and Big Data Technology

The traditional method of analyzing student behavior usually requires manual collection of student behavior data, or extraction of relevant data from school management systems and student information systems, and storage in databases for processing. However, this method is relatively time-consuming and cumbersome. With the development of machine learning algorithms and big data technology, today’s student behavior analysis can be processed and analyzed using corresponding methods and tools. The Hadoop Distributed File System (HDFS) was used to collect and store student behavior data, while Decision Tree and Random Forest were selected to train and test student behavior data to improve the accuracy and effectiveness of student behavior analysis. Finally, student behavior was predicted. This article analyzed student behavior based on machine learning algorithms and big data technology, with a prediction accuracy of up to 95% for student grades. It can also detect and solve student behavior problems in a timely manner, providing targeted guidance and suggestions for students. Student behavior analysis based on machine learning algorithms and big data technology can help students and educators understand their individual differences and needs, provide personalized education and guidance, and promote the optimization of education and the comprehensive development of students.