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.在当今高等教育领域，对学生学习表现的精准评估仍是一项关键挑战。传统确定性评估模型依托二元逻辑（及格/不及格）或僵化的数值化评分体系，往往无法捕捉人类学习过程中固有的模糊性与多面性特质。这一局限性使得采用更具灵活性的计算智能技术成为必要。本研究提出了一种基于模糊逻辑（Fuzzy Logic）理论的综合诊断框架，用于评估学生的知识掌握水平。该模型借助语言变量与模糊推理系统（FIS），将量化指标（考试分数）与质性指标（出勤情况、课堂参与度）相结合，实现对学生能力素养的全方位评估。本研究的方法论流程包括：为输入变量定义隶属度函数、构建基于教学专业经验的规则库，以及采用重心法完成去模糊化操作。初步分析结果表明，这种基于模糊逻辑的评估方法能够显著降低人工评分所带来的主观性，为教育工作者提供更为精细、公平且个性化的诊断工具。