面向CS1例题主动学习的自解释导师
Self-Explanation Tutor for Active Study of CS1 Worked Examples
- University of Pittsburgh（匹兹堡大学）
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究构建了面向入门编程的自解释导师ESSE，证实基于LLM的评估可支撑该导师，且其反馈能提升学生学习例题的效果。
AI中文摘要：
例题是入门编程的重要组成部分，但阅读其中的专家解释属于被动学习。自我解释即学生通过子目标层级分析向自己阐释问题及其解决方案，能将学习转变为主动任务，但该方法难以规模化，因为评估自由文本解释并提供及时反馈缺乏简便的自动化解决方案。本研究探究大型语言模型（LLM）能否填补这一空白。我们构建了面向入门编程的自解释导师ESSE，学生可对例题各行进行解释，并获得LLM针对每条解释的正确性与完整性的即时反馈，同时我们设定两个目标：其一，探究LLM对学生解释的判断是否足以作为该导师的引擎，我们将其判断与两类独立的人类参考标准对比，即单一领域专家及一群非专家评分者，分别具有各自的优势与劣势，明确LLM可靠的领域及存在的系统性偏差；其二，探究基于LLM的辅导是否对学生有益，将其部署于一门入门Java课程后，我们发现其反馈促使学生坚持并修改解释而非放弃，学生的解释在多次尝试中愈发完整且概念更丰富，同时学生展现出学习成效。这些结果表明，基于LLM的评估足以支撑自解释导师，且该导师可积极塑造学生学习例题的方式。
英文摘要：
Worked examples are an important part of introductory programming, but reading their expert explanations is passive. Self explanation, students explaining the problem and its solution to themselves with subgoal level analysis, converts passive reading into an active study of worked example, yet it is hard to scale because assessing free-text explanations and returning timely feedback has had no easy automated solution. We investigate whether a large language model (LLM) can fill that gap. We build a self-explanation tutor for introductory programming, ESSE, in which students explain lines of worked examples and receive immediate LLM feedback on the correctness and completeness of each explanation, and we pursue two goals. First, we ask whether the LLM judges student explanations well enough to serve as the engine of the tutor; we assess its judgments against two independent human reference standards of different kinds, a single domain expert and a crowd of non-expert raters, each with its own strengths and weaknesses, characterizing both where the LLM is reliable and the systematic tendencies in how it diverges. Second, we ask whether the LLM-based tutoring benefits students; deploying it in an introductory Java course, we find that its feedback leads students to persist and revise rather than abandon a line, that their explanations grow more complete and conceptually richer across attempts, and that students show evidence of learning. These indicate that LLM-based assessment is good enough to power a self-explanation tutor, and that the tutor positively shapes how students study worked examples.
