Commonly, introductory programming courses in higher education institutions have hundreds of participating students eager to learn to program. The manual effort for reviewing the submitted source code and for providing feedback can no longer be managed. Manually reviewing the submitted homework can be subjective and unfair, particularly if many tutors are responsible for grading. Different autograders can help in this situation; however, there is a lack of knowledge about how autograders can impact students' overall perception of programming classes and teaching. This is relevant for course organizers and institutions to keep their programming courses attractive while coping with increasing students. This paper studies the answers to the standardized university evaluation questionnaires of multiple large-scale foundational computer science courses which recently introduced autograding. The differences before and after this intervention are analyzed. By incorporating additional observations, we hypothesize how the autograder might have contributed to the significant changes in the data, such as, improved interactions between tutors and students, improved overall course quality, improved learning success, increased time spent, and reduced difficulty. This qualitative study aims to provide hypotheses for future research to define and conduct quantitative surveys and data analysis. The autograder technology can be validated as a teaching method to improve student satisfaction with programming courses.

翻译：通常，高等教育机构中的编程入门课程有数百名渴望学习编程的学生参与。手动审查提交的源代码并提供反馈的工作已难以管理。手动评审提交的作业可能带有主观性且不公平，尤其是在多位助教负责评分的情况下。不同的自动评分器能够应对这种情况；然而，目前尚缺乏关于自动评分器如何影响学生对编程课程和教学整体认知的认知。这对于课程组织者和机构而言至关重要，既要保持编程课程的吸引力，又要应对学生人数的增长。本文研究了多个近期引入自动评分的大型基础计算机科学课程的标准大学评估问卷结果。分析了干预前后的差异。通过结合额外观察，我们假设自动评分器可能如何促成数据的显著变化，例如改善助教与学生间的互动、提升整体课程质量、促进学习成效、增加投入时间以及降低课程难度。这项定性研究旨在为未来研究提供假设，以设计和开展定量调查与数据分析。自动评分器技术可作为教学方法得到验证，从而提高学生对编程课程的满意度。