A Fuzzy Logic-Based Evaluation System for Primary and Secondary Schools Using Multi-Criteria Decision-Making

Abstract

Conventional student assessment methods primarily emphasize academic achievement and often fail to capture the complexity of learners’ cognitive, behavioral, and socio-emotional development. As a result, evaluation outcomes may not accurately represent students’ overall learning progress or effectively support educational decision-making. This study proposes an integrated student evaluation framework that combines Fuzzy Logic (FL) and Multi-Criteria Decision-Making (MCDM) techniques to develop a holistic, transparent, and context-sensitive grading model for students from Grades 1 to 12 in the Dhanbad district of Jharkhand, India. The framework incorporates both quantitative and qualitative criteria, including academic performance, learning effort, conceptual understanding, classroom participation, motivation, and socio-emotional development, providing a comprehensive assessment of student achievement. To address uncertainty and subjectivity in educational evaluation, fuzzy logic converts linguistic assessments into computationally meaningful values. Teachers’ expert judgments are analyzed using fuzzy linguistic variables to examine the influence of educational infrastructure, classroom conditions, and learning resources on student outcomes. The methodology includes designing a fuzzy inference system, developing membership functions, and rule-based knowledge structures. The proposed framework supports evidence-based instructional planning, personalized feedback, and equitable assessment while reducing inconsistencies associated with traditional grading. By integrating computational intelligence with educational evaluation, the study presents a flexible, transparent, and learner-centered assessment model aligned with India’s National Education Policy (NEP) 2020, enabling more accurate and context-responsive student evaluation.