2026· International journal of research and innovation in applied science· Vol 11, pp. 2748-2754· 0 citations
TL;DR
A fuzzy logic-based student evaluation system that considers multiple performance parameters such as attendance, assignment scores, internal assessments, and examination marks is presented.
Abstract
Student evaluation plays a crucial role in academic institutions for assessing learning outcomes and overall performance. Traditional evaluation methods often rely on rigid grading systems that may not accurately represent a student's capabilities. Fuzzy Logic provides an effective alternative by handling uncertainty and imprecision in academic assessment. This paper presents a fuzzy logic-based student evaluation system that considers multiple performance parameters such as attendance, assignment scores, internal assessments, and examination marks. The proposed model uses linguistic variables and fuzzy inference rules to generate a comprehensive performance score. Experimental analysis demonstrates that the fuzzy logic approach provides more realistic and flexible evaluations compared to conventional grading methods.
Uncertainty in instructors' judgments and qualitative evaluations is difficult to capture in traditional student performance evaluations centered on descriptive statistics and averages. While traditional fuzzy systems are capable of handling this kind of ambiguity, they become less efficient and harder to understand as the number of evaluation variables rises since their rule bases are exponentially larger. Exam results may be used to evaluate students' performance, and this research suggests a genetic-fuzzy architecture with rule-base reduction that can be understood. The model was trained in MATLAB using the following parameters: 50 nodes, 25 generations, 0.8 crossover probability, 0.2 mutation rate, and roulette-wheel selection; Mamdani inference; centroid defuzzification; and a genetic algorithm. The non-optimized fuzzy method's MAE is 0.0208 and the genetic-fuzzy method's MAE is 0.0101, according to recalculation utilizing the 25 records shown in Table 1. These values are unique to the descriptive versus creative 25-record comparison and should not be used interchangeably with the separate function membership creative sensitivity results from Table 4. The original basic set of 125 rules was pared down to 46 rules. The leakage-free train-validation-test methodology is defined in this version, and final generalization estimations must be given from it.
Zainab Hammoodi Noori· Journal of Al-Farabi for Eng...· 0 citations
This article examines a soft computing model designed to predict the academic success of first-year students under conditions of educational data uncertainty. The need to combine fuzzy logic, expert assessment, and intelligent analysis of educational data is substantiated. It is shown that academic performance prediction should be based on academic, motivational, behavioral, and social adaptation factors. The proposed model allows for the identification of risk groups, interpretation of the causes of performance decline, and support for management decisions in an educational institution.
Z. Minazova, Tokhiriyon Boisdzhoni, N. N. Danko· SOFT MEASUREMENTS AND COMPUT...· 0 citations
Fuzzy logic is shown to provide more flexible assessments compared to rigid scale procedures, and a system of input linguistic variables, a database of expert rules, and an integrated motivation indicator are proposed.
V. Minazova, N. N. Danko, G. G. Cherentsova· SOFT MEASUREMENTS AND COMPUT...· 0 citations
Educational assessment systems conventionally rely on crisp numerical scores that inadequately capture the gradual and uncertain nature of student learning, and while fuzzy inference systems (FIS) have been applied to address this limitation, prior studies rarely compare alternative membership function designs under identical experimental conditions, leaving open the practical question of which membership function type best supports fuzzy-based educational assessment. This study addresses that gap by presenting a comparative evaluation of Gaussian, Triangular, and Trapezoidal membership functions, which were selected because they are among the most widely used membership functions in fuzzy educational assessment and differ in their smoothness, computational simplicity, and plateau characteristics within a Mamdani Fuzzy Inference System (FIS) for assessing student learning achievement. Three fuzzy models were developed using examination score, assignment completion, and class participation as input variables, while learning achievement served as the output variable. A dataset comprising 360 secondary school students was used for model development and validation. Teacher evaluation scores were employed as benchmark values for assessing model performance. The three models were implemented using identical rule bases, inference mechanisms, and defuzzification procedures, differing only in the type of membership function employed. Model performance was evaluated using the Pearson correlation coefficient, root mean square error (RMSE), and mean absolute error (MAE). The results revealed that the Gaussian model achieved the highest correlation with teacher evaluations (r = 0.9623), indicating superior agreement with teacher assessment patterns, whereas the Triangular model produced the lowest RMSE (7.194) and MAE (4.867), demonstrating greater numerical prediction accuracy.
Abubakar Audu, Adeku Ibrahim Musa, Kehinde Rotimi· FUDMA Journal of Sciences· 0 citations
This work offers a robust decision-support framework for higher education institutions, accreditation bodies, and employers by systematically linking graduates’ knowledge, skills, and behavioral attributes to technical demand by systematically linking graduates’ knowledge, skills, and behavioral attributes to technical demand.
Mary Ann George, A. M. George, Susan G. Varghese et al.· IEEE Access· 0 citations
Purpose – This research aims to develop a Web-Based Smart Quiz System that integrates performance analytics and user behavior monitoring to support more objective and comprehensive learning evaluations. The problem raised is the limitations of the online quiz system in combining fuzzy logic-based performance analysis with exam integrity supervision on the system dashboard.
Design/methods/approach – This system is developed using a prototype model. Student performance and exam integrity were analyzed using a Mamdani fuzzy logic approach based on four input variables, namely score percent, integrity, attempt count, and time ratio. For fuzzy inference processing, warning count, tab switch count and fullscreen exit count were aggregated into a single integrity violations index, which was used as the integrity variable. The system is also equipped with examination integrity features, an analytics dashboard, and question item analytics to support adaptive evaluation and examination integrity assessment.
Findings – The results showed that 81 quiz attempts were classified into 33 Superior, 39 Good, 7 Enough, and 2 Needs Coaching. Risk analysis showed that 79 attempts were in the Safe category, while 2 attempts required further attention. These results indicate that the proposed system can support adaptive performance evaluation while facilitating examination integrity monitoring during online assessment.
Research implications/limitations – The study was conducted within a single higher education institution and relied on predefined membership functions and rule bases. Future studies should involve larger and more diverse populations and explore longitudinal validation of the model.
Originality/value – This study presents a prototype-based integrated assessment platform that combines fuzzy performance evaluation, examination integrity monitoring, user behaviour analytics, and question item analytics within a single web-based Smart Quiz System. The proposed platform is intended as a decision-support tool for learning evaluation and provides a foundation for future validation and enhancement using larger datasets and advanced analytics methods.
I. W. S. Nirawana, Ramaulvi Muhammad Akhyar, Eliyah Achanta Manapa Sampetoding· Information Technology Educa...· 0 citations
