The emergence of large language models (LLMs) has transformed research and practice across a wide range of domains. Within the computing education research (CER) domain, LLMs have garnered significant attention, particularly in the context of learning programming. Much of the work on LLMs in CER, however, has focused on applying and evaluating proprietary models. In this article, we evaluate the efficiency of open-source LLMs in generating high-quality feedback for programming assignments and judging the quality of programming feedback, contrasting the results with proprietary models. Our evaluations on a dataset of students' submissions to introductory Python programming exercises suggest that state-of-the-art open-source LLMs are nearly on par with proprietary models in both generating and assessing programming feedback. Additionally, we demonstrate the efficiency of smaller LLMs in these tasks and highlight the wide range of LLMs accessible, even for free, to educators and practitioners.

翻译：大型语言模型（LLMs）的出现已广泛改变众多领域的研究与实践。在计算教育研究（CER）领域，LLMs 获得了显著关注，尤其在编程学习情境中。然而，CER 中关于 LLMs 的研究大多集中于应用和评估专有模型。本文评估了开源 LLMs 在生成高质量编程作业反馈以及评判编程反馈质量方面的效能，并将结果与专有模型进行对比。我们在学生提交的 Python 入门编程练习数据集上的评估表明，最先进的开源 LLMs 在生成和评估编程反馈方面已接近专有模型的水平。此外，我们展示了较小规模 LLMs 在这些任务中的效率，并强调了教育工作者和实践者能够获取（甚至免费）的 LLMs 的广泛可用性。