Сomparative analysis of grading models using fuzzy logic to enhance fairness and consistency in student performance evaluation

The article examines the continuous assessment of student performance as a crucial element of modern educational processes for achieving learning objectives. Traditional assessment methods, such as exams and grading systems, do not always reflect the diversity of learning styles and individual characteristics of students, creating gaps in fairness and accuracy. As a solution to this issue, the study proposes a Fuzzy Logic Model (FLM), which serves as an innovative approach to assessing student performance. The study, conducted on a sample of 33 students enrolled in the ‘Object-Oriented Programming in Java’ course, compares the effectiveness of the FLM with traditional grading systems such as national standards, arithmetic mean, as well as institutional schemes including the U.S. Grade Point Average system and India’s Central Board of Secondary Education system. The advantage of the FLM lies in its ability to model uncertainty and subjective elements of assessment, making the system more flexible and comprehensive. The results of the study show that the FLM can provide a fairer, more accurate and individualized assessment, better reflecting the complexity and multifaceted nature of student performance. The article emphasizes the importance of continuously improving assessment methods to meet modern educational demands, highlighting the relevance of using adaptive models such as fuzzy logic to enhance educational outcomes and increase the fairness of assessments.