A life cycle model of IoT for higher education based on student and market needs

This paper presents a life cycle model based on Internet of Things (IoT) for higher education. This model includes four stages i.e. data-driven student recruitment, smart learning environments, real-world skill development and continuous graduate support. By using the opensource data for 7751 students about student interactions, educational institutions of higher learning may be able to get a more in-depth understanding of the behavior and preferences of students via the process of data-driven student recruitment. The results show significant improvements in enrollment rates (87.50%, SD = 5.20%), student engagement (mean score = 4.42, SD = 0.67), academic achievement (mean GPA = 3.25, SD = 0.42), and graduate satisfaction (mean score = 4.56, SD = 0.73). The model also demonstrates strong positive correlations between student engagement and academic achievement (r = 0.82, P < 0.001) and employability rate and graduate satisfaction (r = 0.85, P < 0.001). Regression analysis indicates that student engagement is a significant predictor of academic achievement (β = 0.75, SE = 0.05, R² = 0.68, P < 0.001), and employability rate is a significant predictor of graduate satisfaction (β = 0.8, SE = 0.06, R² = 0.72, P < 0.001). The proposed model achieves a pass rate of 92.10% and an employability rate of 88.20%. Overall, the study demonstrates the potential of IoT-based life cycle models to improve student outcomes and satisfaction in higher education.