Fuzzy Intelligent System for Student Software Project Evaluation
This work addresses the problem of manual grading inefficiency and bias in academic software project evaluation for educators and students, but it is incremental as it applies existing fuzzy logic methods to a specific educational context.
The paper tackles the challenge of assessing student performance in software project courses by introducing a fuzzy intelligent system that automates evaluation based on criteria like clean code, inheritance use, and functionality, demonstrating promising results in standardizing grading and reducing subjective bias.
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 the size of classes increases. The current paper introduces a fuzzy intelligent system designed to evaluate academic software projects using object-oriented programming and design course as an example. To establish evaluation criteria, we first conducted a survey of student project teams (n=31) and faculty (n=3) to identify key parameters and their applicable ranges. The selected criteria - clean code, use of inheritance, and functionality - were selected as essential for assessing the quality of academic software projects. These criteria were then represented as fuzzy variables with corresponding fuzzy sets. Collaborating with three experts, including one professor and two course instructors, we defined a set of fuzzy rules for a fuzzy inference system. This system processes the input criteria to produce a quantifiable measure of project success. The system demonstrated promising results in automating the evaluation of projects. Our approach standardizes project evaluations and helps to reduce the subjective bias in manual grading.