KNIME-Based Correlation Analysis of Computer Science Courses and Graduation Projects in Vocational Education

This study investigates the correlation between course scores and graduation project performance among students in a Computer Network Technology program at a vocational university. Using data from two classes enrolled in 2021, the research employs rank correlation analysis and XGBoost-based prediction modeling with cross-validation to identify key factors influencing graduation project outcomes. The results reveal that courses such as Linux OS, Cloud Computing, and Java exhibit strong correlations with graduation project success. Furthermore, the XGBoost model achieved an overall accuracy of 80.4% in predicting HD grade attainment, though it showed a tendency to overpredict HD outcomes. These findings provide insights for curriculum optimization and teaching strategies to enhance student performance in graduation projects. The study also highlights the importance of aligning course content with industry trends in “cloud computing, big data, IoT, AI, and blockchain” to better prepare students for future employment in the manufacturing sector.