CONFERENCEhttps://doi.org/10.22364/htqe.2026.05 | 84-98 | PDF
An Integrated DEA–Fuzzy Logic Framework for Curriculum Performance Evaluation
Fatma Yaşlı Şen1
1 Eskisehir Technical University, Turkiye
Abstract
This study proposes an integrated DEA–Fuzzy Logic framework for the systematic and quantitative evaluation of curriculum performance within a multi-criteria decision-making context. The framework addresses two key challenges in curriculum assessment: the subjectivity of expert judgments and the need to determine the relative importance and efficiency of courses across multiple competency criteria. For the evaluation, a performance analysis was conducted by considering the weekly course hours and credits of the courses, along with the levels of contribution they provide to the defined competency areas associated with the programs in which they are offered. The contribution of each course to these competencies is evaluated using a seven-level linguistic scale via the experts. To manage uncertainty and ambiguity in qualitative assessments, triangular fuzzy numbers are employed, converting linguistic judgments into measurable performance scores. This process generates a structured and comparable multi-criteria dataset for the Data Envelopment Analysis (DEA) model. Courses are treated as decision-making units, where weekly course hours and course credits are considered as input variables, while the quantified competency-areas scores constitute the output variables. Using an output-oriented Variable Returns to Scale (VRS) DEA model, the relative efficiency of courses is assessed without requiring predefined weights. This non-parametric optimization approach enables courses to be ranked according to the extent to which they contribute to curriculum competencies relative to the instructional resources allocated to them. The proposed framework is applied to an associate degree vocational education curriculum as a case study to demonstrate its operational validity. The findings indicate that the integrated DEA–Fuzzy Logic approach provides a robust, flexible, and decision-supportive tool for evidence-based curriculum evaluation.
Keywords: Curriculum, Performance Evaluation, Data Envelopment Analysis (DEA), Fuzzy Logic, Multi-Criteria Decision Making (MCDM)
This work was supported by the Eskişehir Technical University Scientific Research Projects Commission under Project No. 25ADP113.
In: Human, Technologies and Quality of Education, 2026. Proceedings of Scientific Papers = Cilvēks, tehnoloģijas un izglītības kvalitāte, 2026. Rakstu krājums. Ed. L. Daniela. Riga: University of Latvia, 2026. 236 p.
ISBN 978-9934-36-685-7
https://doi.org/10.22364/htqe.2026