The 2020 pandemic imposed new demands on teaching practices to support student's distance learning process. In this context, automated assessment (AA) is a pivotal resource that offers immediate and automatic feedback on students' programming tasks. Although the literature provides several contributions regarding AA of Programming Exercises (PEs), very few works discuss the automatic generation of personalized PE. This study reports our experience in applying a new proposal for AA-PE in an Introduction to Programming (IP) course for a large group of students. This proposal's key feature is the ability to apply AA-PE and parameterized unified exams to different programming languages by using the open-source tools MCTest and Moodle (with virtual programming lab [VPL] plugin). During the first quarter of 2019, teachers of 19 of 44 IP-FF (face-to-face) classes embraced our approach as a component in their pedagogical intervention. These classes achieved a higher pass rate (67.5%) than those that did not adopt our AA solution (59.1%), whereas the standard deviation was quite the same (22.5% and 21.3%, respectively). Additionally, preliminary results revealed a strong linear correlation (r = .93) between the pass rate and the average grade of the AA-PE. In IP-BL (blended learning), two classes used our method in the exams, with 171 students and a pass rate of 70.4%. These results corroborate previous works that continuous assessment combined with immediate feedback can contribute to students' learning process.

中文翻译：

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大型入门编程课程自动化测评的体会

2020 年的大流行对教学实践提出了新的要求，以支持学生的远程学习过程。在这种情况下，自动评估 (AA) 是一种关键资源，可为学生的编程任务提供即时和自动反馈。尽管文献提供了一些关于编程练习（PE）的 AA 的贡献，但很少有作品讨论个性化 PE 的自动生成。本研究报告了我们在面向大量学生的编程导论 (IP) 课程中应用 AA-PE 新提案的经验。该提案的主要特点是能够通过使用开源工具 MCTest 和 Moodle（带有虚拟编程实验室 [VPL] 插件）将 AA-PE 和参数化统一考试应用于不同的编程语言。在 2019 年第一季度，44 个 IP-FF（面对面）课程中有 19 个的教师将我们的方法作为他们教学干预的一个组成部分。这些班级的通过率 (67.5%) 比未采用我们的 AA 解决方案的班级 (59.1%) 更高，而标准差却完全相同（分别为 22.5% 和 21.3%）。此外，初步结果显示出很强的线性相关性（r  = .93) 在合格率和 AA-PE 的平均成绩之间。在IP-BL（混合学习）中，有两个班级在考试中使用了我们的方法，共有171名学生，通过率为70.4%。这些结果证实了之前的工作，即持续评估与即时反馈相结合可以促进学生的学习过程。