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.

翻译：开发软件项目有助于学生将知识付诸实践并培养团队协作能力。然而，在以项目为导向的课程中评估学生表现面临重大挑战，尤其是在班级规模不断扩大的情况下。本文介绍了一种模糊智能系统，旨在以面向对象编程与设计课程为例，评估学术软件项目质量。为建立评估标准，我们首先对学生项目团队（n=31）和教师（n=3）进行调研，以确定关键参数及其适用范围。最终选定"整洁代码"、"继承机制使用"和"功能性"作为评估学术软件项目质量的核心准则。这些准则被表示为带有对应模糊集的模糊变量。通过与三位领域专家（包括一位教授和两位课程讲师）协作，我们为模糊推理系统定义了一组模糊规则。该系统将输入准则处理为可量化的项目成功度量指标。实验表明，该系统在项目评估自动化方面展现出良好效果。我们的方法不仅规范了项目评估流程，还有助于减少人工评分中的主观偏差。