Data-driven evaluation of learning outcomes in a software-defined networking course

Software-defined networking (SDN) is a key paradigm in modern networking that separates the control and data planes to enable centralized, programmable, and flexible network management. Its growing importance in 5G and emerging 6G environments underscores the need for higher-education institutions to prepare future professionals to effectively apply programmable networking technologies. This study empirically evaluates the educational outcomes of an undergraduate SDN course within a technology-enhanced learning framework using a convergent mixed-methods approach that integrates quantitative performance indicators and qualitative student perceptions, supported by descriptive, correlational, and multiple regression analyses. Results indicate that prior programming proficiency and networking foundations are associated with student success. Python knowledge correlated with achievement but did not remain an independent predictor after controlling for other variables, while laboratory engagement enhanced conceptual understanding, motivation, and perceived usefulness of the course, confirming the value of experiential learning in SDN education. Students particularly valued interactive laboratory sessions and real-world case studies that supported deeper conceptual understanding. Although controller logic and flow-table design were perceived as the most challenging topics, overall satisfaction remained high with no significant gender differences. The findings provide empirical support for data-driven curriculum design in technology-enhanced SDN education, showing that the integration of hands-on laboratory activities, learning analytics, and quantitative performance evaluation can inform targeted improvements in course design and instructional support. These results offer practical guidance for educators designing effective SDN learning experiences in higher education contexts globally.