International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Enhancing Student Performance Evaluation Using a Hybrid Fuzzy Inference Model with Optimized Rule Base

Author(s) MRS ALKA AGARWAL, Prof. SHUBHNESH KUMAR GOYAL
Country India
Abstract This study develops an improved fuzzy inference-based framework for multi-criteria evaluation of student performance. The proposed system integrates five major academic indicators—attendance, assignment scores, practical performance, midterm examination results, and viva assessment—to provide a comprehensive evaluation mechanism. A hybrid structure of trapezoidal and Gaussian membership functions is employed to effectively model both structured and subjective inputs.
The system is implemented using a Mamdani inference approach, along with an optimized rule base to minimize redundancy and computational complexity. The final performance score is obtained using centroid defuzzification. To validate the effectiveness of the model, statistical measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and correlation analysis are applied. Experimental findings demonstrate that the proposed fuzzy model produces more consistent and accurate results compared to traditional evaluation methods. The framework successfully captures nonlinear relationships among performance indicators and supports adaptive and human-like decision-making. This approach can significantly enhance intelligent educational systems by enabling fair assessment and personalized learning strategies. This framework can support intelligent educational systems by enabling fair evaluation and facilitating personalized academic interventions.
Keywords Fuzzy Logic, Fuzzy Inference System, Student Performance Evaluation, Multi-Criteria Decision Making, Educational Data Mining, Defuzzification, Academic Analytics
Field Mathematics > Logic
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-26
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.79121
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E-ISSN 2582-2160
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