Evaluating an education model via Pythagorean fuzzy AHP and decision tree

Applied education models aim to prepare students for professional careers by bridging the gap between theoretical knowledge and practical experience. However, existing literature on these models has mostly focused on expert and institutional perspectives, with limited consideration given to students' perceptions and expectations. This study examines an applied education model implemented at an industrial university. Taking a holistic approach, the study evaluates student feedback and the opinions of academic and industry experts to fill this gap in the literature. The research used a hybrid methodological framework combining the Pythagorean Fuzzy Analytic Hierarchy Process and a decision tree algorithm. The data were obtained from 76 students and 13 academic and industry experts. The Pythagorean Fuzzy Analytic Hierarchy Process was used to determine the weights of the evaluation criteria based on the opinions of students and experts, and the decision tree algorithm was used to analyze the structural and predictive relationships between the criteria. Correlation analysis was also performed to examine the relationships between the criteria. The findings suggest that the criteria of ‘Developing a foreign language’, ‘Developing a network’, ‘Graduating with a high average’, and ‘Developing program/software skills’ significantly impact students’ five-year career planning. The results reveal that the dimensions of academic guidance, industry collaboration, and skills development form a complementary structure. This study makes a theoretical contribution to the literature on applied education by presenting a multi-criterion, data-driven analysis model that integrates student perspectives with expert evaluations to develop feasible suggestions for strengthening the alignment between higher education and industry.