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探索自动化反馈在编程教育中的作用：一项系统文献综述

Exploring the Role of Automated Feedback in Programming Education: A Systematic Literature Review

Yeonji Jung, Yunseo Lee, Jiyeong Bae, DoYong Kim, Heungsoo Choi, Minji Kang, Unggi Lee

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AI总结：

本文通过系统文献综述，探讨自动化反馈在编程教育中的作用，发现其主要聚焦于错误检测和代码正确性，但缺乏对更高阶学习过程的支持。

摘要：

自动化反馈系统在编程教育中已成为越来越重要的组成部分，学习者在代码构建、测试和细化之间进行迭代循环。尽管其在实践和技术层面的整合日益广泛，尤其是在人工智能领域，该领域的研究仍显得碎片化，缺乏在技术与教学维度上的整合。本系统文献综述综合了截至2024年9月发表的61项实证研究，从五个维度对自动化反馈系统进行了概念性的分析：系统架构、教学功能、交互机制、情境部署和评估方法。研究发现，大多数系统都是全自动的，嵌入在线平台中，主要关注错误检测和代码正确性。尽管最近的发展整合了自适应功能和大型语言模型，以实现更加个性化和互动的反馈，但很少有系统提供支持更高阶学习过程、互动组件或学习者自主性。此外，评估实践往往强调短期性能提升，对长期结果或教学整合的关注有限。这些发现呼吁对自动化反馈不再视为错误纠正的技术附加功能，而是作为教学支架，支持更深入、适应性和互动性的学习。

英文摘要：

Automated feedback systems have become increasingly integral to programming education, where learners engage in iterative cycles of code construction, testing, and refinement. Despite its wider integration in practices and technical advancements into AI, research in this area remains fragmented, lacking synthesis across technological and instructional dimensions. This systematic literature review synthesizes 61 empirical studies published by September 2024, offering a conceptually grounded analysis of automated feedback systems across five dimensions: system architecture, pedagogical function, interaction mechanism, contextual deployment, and evaluation approach. Findings reveal that most systems are fully automated, embedded within online platforms, and primarily focused on error detection and code correctness. While recent developments incorporate adaptive features and large language models to enable more personalized and interactive feedback, few systems offer support for higher-order learning processes, interactive components, or learner agency. Moreover, evaluation practices tend to emphasize short-term performance gains, with limited attention to long-term outcomes or instructional integration. These findings call for a reimagining of automated feedback not as a technical add-on for error correction, but as a pedagogical scaffold that supports deeper, adaptive, and interactive learning.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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