来自社会塑造理论的洞见：大型语言模型在本科编程课程中的采纳
Insights from Social Shaping Theory: The Appropriation of Large Language Models in an Undergraduate Programming Course
AI总结：
本研究基于社会塑造理论，通过调查、访谈和成绩数据发现，本科编程课程中学生对LLM的使用受职业预期和同伴感知影响，且早期使用与较低自我效能感和期中成绩相关，过度依赖感知则与后期自我效能下降相关。
摘要：
大型语言模型（LLMs）生成、调试和解释代码的能力引发了研究者和教育者对本科编程教育的兴趣，许多人预期其在编程教育中具有变革潜力。然而，关于为何以及如何在编程教育中使用LLMs的决策，可能不仅仅涉及对LLM技术能力的评估。本研究以技术的社会塑造理论作为指导框架，探讨学生的社会认知如何影响他们自身对LLM的使用。随后，我们考察了自我报告的LLM使用情况与学生在本科编程课程中的自我效能感和期中成绩之间的相关性。通过三角互证来自匿名课程结束学生调查（n=158）、课程中期自我效能感调查（n=158）、学生访谈（n=10）、作业中自我报告的LLM使用情况以及期中成绩的数据，我们发现学生对LLM的使用与其对未来职业的期望以及他们对同伴使用情况的感知相关。此外，在我们的研究情境中，早期自我报告的LLM使用与较低的自我效能感和较低的期中分数相关，而学生感知到的对LLM的过度依赖——而非使用本身——与课程后期自我效能感的下降相关。
英文摘要：
The capability of large language models (LLMs) to generate, debug, and explain code has sparked the interest of researchers and educators in undergraduate programming, with many anticipating their transformative potential in programming education. However, decisions about why and how to use LLMs in programming education may involve more than just the assessment of an LLM's technical capabilities. Using the social shaping of technology theory as a guiding framework, our study explores how students' social perceptions influence their own LLM usage. We then examine the correlation of self-reported LLM usage with students' self-efficacy and midterm performances in an undergraduate programming course. Triangulating data from an anonymous end-of-course student survey (n = 158), a mid-course self-efficacy survey (n=158), student interviews (n = 10), self-reported LLM usage on homework, and midterm performances, we discovered that students' use of LLMs was associated with their expectations for their future careers and their perceptions of peer usage. Additionally, early self-reported LLM usage in our context correlated with lower self-efficacy and lower midterm scores, while students' perceived over-reliance on LLMs, rather than their usage itself, correlated with decreased self-efficacy later in the course.
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