CONCEPTUAL FOUNDATIONS AND MATHEMATICAL MODELING OF STUDENT KNOWLEDGE DIAGNOSIS USING FUZZY LOGIC SYSTEMS

In the contemporary landscape of higher education, accurate assessment of student performance remains a critical challenge. Traditional deterministic assessment models, which rely on binary logic (Pass/Fail) or rigid numerical grading, often fail to capture the inherent ambiguity and multifaceted nature of human learning. This limitation necessitates the adoption of more flexible computational intelligence techniques. This study proposes a comprehensive diagnostic framework based on Fuzzy Logic theory to evaluate student knowledge. By utilizing linguistic variables and fuzzy inference systems (FIS), the proposed model integrates quantitative metrics (test scores) with qualitative indicators (attendance, classroom engagement) to produce a holistic assessment of student competency. The research methodology involves defining membership functions for input variables, constructing a rule base derived from pedagogical expertise, and applying the centroid method for defuzzification. Preliminary analysis suggests that this fuzzy-based approach significantly reduces the subjectivity associated with human grading and provides a more granular, equitable, and personalized diagnostic tool for educators.