用大模型辅助分析文本，让教育研究者高效开展大规模质性研究。
Empowering Computing Education Researchers Through LLM-Assisted Content Analysis
- 结合内容分析与大语言模型，实现大规模文本的自动化编码与归纳。
- 在计算教育数据集上验证方法可复现且结果可靠，提升研究覆盖面。
- 适合缺乏资源的研究者，推动教育实践与研究质量共同提升。
计算教育研究（CER）常由一线教师发起，旨在改进教学实践。然而，许多研究者受限于同行支持、资源或能力，难以开展具有普遍意义或足够严谨的研究。为应对这一挑战，本文提出一种改进的大型语言模型辅助内容分析（LACA）方法，将内容分析与大语言模型结合，使研究者能够在不增加负担的前提下，对大规模文本数据进行严谨分析。基于一个计算教育数据集，我们展示了LACA如何以可复现、严谨的方式实施。该方法有望在CER领域扩大研究规模，促进更具普适性的发现。未来类似方法的发展，将有助于提升教育实践与研究的整体质量。
原文摘要 · Abstract (English)
Computing education research (CER) is often instigated by practitioners wanting to improve both their own and the wider discipline's teaching practice. However, the latter is often difficult as many researchers lack the colleagues, resources, or capacity to conduct research that is generalisable or rigorous enough to advance the discipline. As a result, research methods that enable sense-making with larger volumes of qualitative data, while not increasing the burden on the researcher, have significant potential within CER. In this discussion paper, we propose such a method for conducting rigorous analysis on large volumes of textual data, namely a variation of LLM-assisted content analysis (LACA). This method combines content analysis with the use of large language models, empowering researchers to conduct larger-scale research which they would otherwise not be able to perform. Using a computing education dataset, we illustrate how LACA could be applied in a reproducible and rigorous manner. We believe this method has potential in CER, enabling more generalisable findings from a wider range of research. This, together with the development of similar methods, can help to advance both the practice and research quality of the CER discipline.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。