理解计算机科学子领域中大学生对大型语言模型的使用
Understanding Student Use of Large Language Models Across Computer Science Subfields
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中文总结 AI 辅助
本研究通过跨子领域分析211名本科生的作业反思数据，发现LLM使用因作业而异，学生多将其视为辅助工具并普遍验证，且验证策略随任务类型变化，表明作业特征对塑造LLM互动与评估至关重要。
中文摘要 AI 辅助
本研究全文论文考察了本科生在计算机科学子领域中如何使用大型语言模型（LLMs）。随着LLMs日益融入计算教育，理解其使用在不同技术和教学情境中的差异对于设计有效的、针对子领域的教学至关重要。本文对一门问题解决课程中的211名本科生的LLM使用情况进行了跨子领域分析，该课程通过结构化教学和反思有意设计以支持负责任且有效的LLM使用。利用跨七个教学模块（涵盖多个计算机科学子领域）的作业后反思数据，我们考察了提示数量、LLM角色概念化和验证行为。结果显示，LLM的采用因作业而异，算法和Web开发中的使用率较高，而软件工程中的使用率较低。学生主要将LLM视为辅助工具而非权威来源，验证在所有子领域中都很常见，大多数学生使用多种策略。验证行为也因作业情境而异，在结构化任务中测试更常见，而在开放式任务中网络搜索更常见。这些发现表明，作业特征在塑造学生如何与LLM输出互动和评估方面起着核心作用，即使在单一、一致应用的 instructional design 下也是如此。本研究提供了关于LLM采用、角色概念化和验证行为如何在计算机科学子领域中变化的实证证据，扩展了我们先前关于结构化、反思性LLM教学的工作，以显示其效果因任务而异，而不仅仅是在总体上。
英文摘要
This research full paper examines how undergraduate students use large language models (LLMs) across computer science subfields. As LLMs become increasingly integrated into computing education, understanding how their use varies across technical and pedagogical contexts is essential for designing effective, subfield-aware instruction. This paper presents a cross-subfield analysis of LLM usage among 211 undergraduate students in a problem-solving course intentionally designed to support responsible and effective LLM use through structured instruction and reflection. Using post-assignment reflection data collected across seven instructional modules spanning multiple computer science subfields, we examine prompt counts, LLM role conceptualization, and verification behavior. Results show that LLM adoption varies substantially by assignment, with higher usage in algorithms and web development and lower usage in software engineering. Students predominantly treat LLMs as assistive tools rather than authoritative sources, and verification is common across all subfields, with most students using multiple strategies. Verification behavior also varies by assignment context, with testing more common in structured tasks and web search more common in open-ended tasks. These findings suggest that assignment characteristics play a central role in shaping how students interact with and evaluate LLM outputs, even under a single, consistently applied instructional design. This work contributes empirical evidence on how LLM adoption, role conceptualization, and verification behavior vary across computer science subfields, extending our prior work on structured, reflective LLM instruction to show how its effects differ by task rather than only in aggregate.
发表机构
- Virginia Tech（弗吉尼亚理工大学）
机构由 AI 辅助整理，请以论文原文为准。
