Enhancing College Student Education and Management through Semisupervised Learning.

College student education and management can be enhanced through a data‐driven approach involving student surveys, academic records, and text analysis to understand student interests and concerns. Effective categorization of relevant topics enables universities to provide tailored support and educational content, thus improving the quality of education and fostering student success and well‐being by adapting to evolving student needs and aspirations. The primary contribution of this work is demonstrating the effectiveness of semisupervised learning methods in educational content classification, providing a robust solution for enhancing college student education and management with limited labeled data. The objective of this study was to evaluate the feasibility of using semisupervised learning methods in educational content classification using the Yahoo Answers dataset. For the Yahoo_500 dataset, the supervised neural network achieved a best evaluation accuracy of 0.6565, an average precision of 0.6539, an average recall of 0.6565, and an average F1 score of 0.6547. In contrast, semisupervised approaches, Dash, FixMatch, and FreeMatch, consistently demonstrated superior performance. Among the evaluated semisupervised architectures, FreeMatch achieved the highest best evaluation accuracy (0.6759), average precision (0.6739), average recall (0.6759), and average F1 score (0.6744). The Yahoo_2000 dataset, which benefited from an increased labeled data pool, exhibited a similar trend with semisupervised approaches consistently surpassing the supervised approach. FreeMatch maintained its top performing position in several categories, including computers and Internet, consumer electronics, and business and finance, with impressive F1 score of ≥0.753. Overall, the semisupervised approaches prove highly effective in improving model performance, highlighting its practical advantages. These results underscore the robustness of semisupervised approaches and their capability to improve classification performance even with limited labeled data. Employing semisupervised learning on the Yahoo Answers dataset and additional data sources for college student management and education can be a powerful tool for gaining insights into students' interests and concerns. [ABSTRACT FROM AUTHOR]