Courses in computer science curricula often involve group programming assignments. Instructors are required to take several decisions on assignment setup and monitoring, team formation policies, and grading systems. Group programming projects provide unique monitoring opportunities due to the availability of both product and process data, as well as challenges in team composition, with students of varying levels of prior programming experience. To gain insights into the experiences and perceptions of students about the assignment setup and grading policies in group programming projects, we interviewed 20 computer science students from four universities. The thematic analysis highlighted factors in group composition that are considered important, as well as advantages and disadvantages of the self-selection of the teams. It also indicated three grading strategies experienced by the students, namely, being assigned the same group grade, individual grades distributed by the instructor, and grade distribution determined by the team, with perceptions about them varying greatly. Several practices for monitoring team contributions were identified. Checking the source code repositories was considered useful in recognizing slacking members, but automated metrics are not always representative of the work distribution. The analysis also uncovered student perceptions on the grading factors for programming assignments, including coding efficiency and skill.

中文翻译：

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学生对小组编程作业设置和评分感知的实证研究

计算机科学课程中的课程通常涉及小组编程作业。教师需要就作业设置和监控、团队组建政策和评分系统做出多项决定。由于产品和过程数据的可用性以及团队组成方面的挑战，小组编程项目提供了独特的监控机会，学生具有不同程度的先前编程经验。为了深入了解学生对小组编程项目中作业设置和评分政策的体验和看法，我们采访了来自四所大学的 20 名计算机科学专业的学生。主题分析突出了小组组成中被认为重要的因素，以及团队自我选择的优缺点。它还指出了学生经历的三种评分策略，即分配相同的小组成绩、教师分配的个人成绩和团队确定的成绩分配，对它们的看法差异很大。确定了监测团队贡献的几种做法。检查源代码存储库被认为有助于识别懈怠的成员，但自动化指标并不总是代表工作分配。该分析还揭示了学生对编程作业评分因素的看法，包括编码效率和技能。对它们的看法差异很大。确定了监测团队贡献的几种做法。检查源代码存储库被认为有助于识别懈怠的成员，但自动化指标并不总是代表工作分配。该分析还揭示了学生对编程作业评分因素的看法，包括编码效率和技能。对它们的看法差异很大。确定了监测团队贡献的几种做法。检查源代码存储库被认为有助于识别懈怠的成员，但自动化指标并不总是代表工作分配。该分析还揭示了学生对编程作业评分因素的看法，包括编码效率和技能。