Fuzzy Intelligent System for Student Software Project Evaluation

Developing software projects allows students to put knowledge into practice and gain teamwork skills. However, assessing student performance in project-oriented courses poses significant challenges, particularly as class sizes increase. This paper introduces a fuzzy intelligent system designed to evaluate academic software projects using an object-oriented programming and design course as an example. Our methodology involved conducting a survey of student project teams (n=31) and faculty (n=3) to identify key evaluation parameters and their applicable ranges. The critical criteria—clean code, use of inheritance, and functionality—were represented as fuzzy variables with corresponding fuzzy sets. We collaborated with three experts, including one professor and two course instructors, to define a set of fuzzy rules for a fuzzy inference system. This system processes the input criteria to produce a quantifiable measure of project success. Our fuzzy intelligent system demonstrated promising results in automating project evaluation, standardizing assessments, and reducing subjective bias in manual grading. The key findings show that the system effectively manages the increasing instructor workload, provides consistent and transparent evaluations, and offers timely and accurate feedback to students.