Evaluating Traditional and Smart Learning Models: A Data-Driven Comparison from Students and Educators

Abstract

The rapid development of educational technologies has introduced new models of teaching and learning challenging the dominance of traditional class perspectives. The purpose of this study is to analyze perceptions from students, teachers and administrators to compare traditional, smart and mixed learning approaches. The data was collected through a structured survey and analyzed using descriptive figures, reliability testing and one-way anova to identify significant differences in stake satisfaction. The results indicate that while traditional education remains moderately effective and enhances smart learning interaction and visualization, mixed education continuously performs better in terms of flexibility, connectivity and overall satisfaction. The ANOVA results confirm that these differences are statistically important (P <0.05). Unlike pre-studies, which focus on a large-scale student's approach, this research incorporates multi-interestful insight, which offers a comprehensive evaluation of the learning approach. Conclusions contribute to empirical evidence supporting mixed learning as a model prepared for the future that integrates the strength of both traditional and technology-prosperous methods. These results highlight the importance of balanced educational strategies and provide actionable insights for institutions that transition towards more effective teaching ecosystems.