A faculty performance evaluation model based on MACBETH and fuzzy filter ranking methods

The establishment of a robust faculty performance evaluation system has become a research hotspot, as it is crucial for the continuous improvement and sustainable development of higher education institutions. However, according to available literature, the need for an easy-to-use management tool for faculty performance evaluation-one that accounts for professional tracks-within the context of China's application-oriented universities remains unaddressed. This study presents a new evaluation framework for measuring and ranking faculty performance, tailored to the characteristics of application-oriented universities. The proposed model applies hybrid multi-criteria decision making (MCDM) techniques through a three-step approach. First, a set of measurement indicators is developed based on a comprehensive review of existing literature and faculty assessment forms used by application-oriented universities in practice. Second, in line with the classification of faculty members' career tracks, the MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) method is utilized to determine the weights of evaluation criteria for each professional track. Finally, the FFR (Fuzzy Filter Ranking) approach is employed to rank faculty performance and identify outstanding faculty award winners. The applicability and utility of the proposed methodology are validated through a case study of an application-oriented university in China, which demonstrates its value as an effective evaluation tool and decision-aiding reference for faculty performance assessment. This study offers an intuitive and readily applicable solution for university stakeholders, streamlining the processes of weight assignment and faculty ranking within performance evaluations. Moreover, it can be extended to address other evaluation problems in academia, thereby contributing to the enhancement of educational quality.