ItemConference Paper

Fuzzy Logic-AHP Hybrid Model for Faculty Performance Evaluation to Enhance Educational Quality in Higher Education

Title
Fuzzy Logic-AHP Hybrid Model for Faculty Performance Evaluation to Enhance Educational Quality in Higher Education
Creator
Kunwar, Fateh Bahadur; John, Tegil J.; Krishna, G. Venkata; Reddy, Kotte Amaranadha; Srinivas, T. Aditya Sai; Bharti, Sandeep
Description
Guaranteeing equitable and precise evaluation of teacher performance is a continual challenge in higher education, as subjective discrimination, uneven metrics, and absence of cohesive frameworks frequently obstruct informed decision-making. A hybrid Fuzzy Logic-Analytic Hierarchy Process (AHP) model is developed to integrate systematic requirement weighting through AHP with the uncertainty management features of fuzzy logic. The method assesses faculty performance across various dimensions, including classroom effectiveness, research output, service involvement, and professional advancement. The integration guarantees impartiality in criterion weighing and adaptability in managing qualitative assessments, resulting in a balanced and thorough evaluation method. The proposed hybrid framework, in contrast to standard models, reduces subjectivity, improves interpretability, and provides greater accuracy in prediction. Experimental findings indicate that the model attains an Accuracy of 96.8%, Precision of 97.2%, Recall of 96.5%, F1score of 96.8%, and AUC of 0.98, surpassing baseline methods like Decision Trees, Logistic Regression, and Support Vector Machines. These findings confirm the resilience and flexibility of the proposed methodology in practical teacher evaluation contexts. The research enhances educational quality and facilitates the integration of hybrid decision-support systems into institutional policy-making and future academic performance evaluations. 2025 IEEE.
Source
2025 IEEE International Conference on Emerging Trends in Computing and Communication, ETCOM 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Analytic Hierarchy Process (AHP); Criteria Weighting; Educational Quality; Faculty Performance Evaluation; Fuzzy Inference System; Fuzzy Logic; Higher Education Analytics; Hybrid Model; Intelligent Decision Support System; Machine Learning in Education; Membership Functions; Multi-Criteria Decision Making; Robustness Analysis; Sensitivity Analysis
Coverage
Kunwar F.B., Greater Noida Institute of Technology, Department of Computer Science and Engineering, Uttar Pradesh, Greater Noida, India; John T.J., Christ University, Department of Computer Science, Karnataka, Bengaluru, India; Krishna G.V., Prasad V Potluri Siddhartha Institute of Technology, Department of CSE (AI&ML), Vijayawada, India; Reddy K.A., School of Advanced Sciences, Kalasalingam Academy of Research and Education, Department of Mathematics, Tamil Nadu, Virudhunagar, India; Srinivas T.A.S., Ravindra College of Engineering for Women, Department of CSE, Kurnool, India; Bharti S., Deewan College, Department of Computer Science and Engineering (CSE), Uttar Pradesh, Meerut, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158508-2;
Format
online
Language
English
Type
Conference paper

Citation

Kunwar, Fateh Bahadur; John, Tegil J.; Krishna, G. Venkata; Reddy, Kotte Amaranadha; Srinivas, T. Aditya Sai; Bharti, Sandeep, “Fuzzy Logic-AHP Hybrid Model for Faculty Performance Evaluation to Enhance Educational Quality in Higher Education,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25835.

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