Construction and clustering analysis of a blended learning evaluation system for higher vocational schools based on AHP-EWM

Abstract

Blended learning has become a crucial direction for the reform of higher vocational education. However, current evaluation systems still have several shortcomings in indicator construction, weight allocation, and personalized teaching feedback. This study constructs a blended learning evaluation system for project-based courses in higher vocational education based on AHP-EWM, covering three primary indicators: learning participation, learning activity, and learning attainment, along with nine secondary indicators. First, a scientific evaluation indicator system is designed by integrating the “three phases with nine steps” blended learning model, and the AHP and EWM combined weighting method is used to determine indicator weights, balancing expert judgment and objective data analysis. Second, by collecting and analyzing student data from mechanical manufacturing technology courses, clustering analysis is employed to identify different learning groups. The results show that this system accurately reflects students’ learning behaviors and provides targeted instructional recommendations for different student types. Finally, this study proposes personalized teaching optimization strategies based on clustering analysis to improve the quality of blended learning. The research findings provide both theoretical support and practical guidance for evaluating blended learning in vocational education institutions.