Data-Driven Decision Support System Analysis in Vocational Education

The design and development of a decision support system for education and teaching originated from the need to address issues such as scattered data, delayed analysis, and insufficient decision-making basis in the educational process. The system is based on educational big data and uses the Hadoop distributed storage framework to process heterogeneous data. It implements learning behavior analysis through an improved K-means clustering algorithm and constructs a random forest prediction model for academic early warning. In a practical application at a vocational technical college, the system performed real-time monitoring and analysis of 32 majors and 428 courses, tracking the learning trajectories of 12,000 students, with a warning accuracy rate of 93.2%. After one year of application, the system helped improve teaching efficiency by 32.5%, course resource utilization by 28.7%, and student learning enthusiasm by 41.2%, resulting in an annual cost savings of 4.85 million yuan. The system provides effective technical support and decision-making basis for improving education and teaching quality.