数据科学研究生通过分析学习数据，揭示了个性化学习洞察。
Data Science Students Perspectives on Learning Analytics: An Application of Human-Led and LLM Content Analysis
- 结合人类分析与大模型技术，处理学生提交的分析成果。
- 12个小组共72人，形成7类主题问题，结果具一致性共识。
- 大模型提供细节洞察，适合教育数据研究者参考。
本研究是英国某大学系列举措之一，旨在深入理解数据科学研究生对学习分析的独特需求。研究采用定性方法，将检索增强生成（RAG）与大型语言模型（LLM）技术结合人类主导的内容分析，基于72名研究生在12个小组中对开放大学学习分析数据集（OULAD）的协作分析成果，获取学生视角。各组均采用结构化数据科学方法，提出的问题被归纳为7个主题，反映其关注领域。尽管变量选择存在差异，但对关联性的解释结果呈现共识。研究发现，数据科学专业学生对学习分析有更深入理解，能通过分析推断有效表达兴趣。人类分析提供整体认知，而LLM则带来更细致的洞察。
原文摘要 · Abstract (English)
Objective This study is part of a series of initiatives at a UK university designed to cultivate a deep understanding of students' perspectives on analytics that resonate with their unique learning needs. It explores collaborative data processing undertaken by postgraduate students who examined an Open University Learning Analytics Dataset (OULAD). Methods A qualitative approach was adopted, integrating a Retrieval-Augmented Generation (RAG) and a Large Language Model (LLM) technique with human-led content analysis to gather information about students' perspectives based on their submitted work. The study involved 72 postgraduate students in 12 groups. Findings The analysis of group work revealed diverse insights into essential learning analytics from the students' perspectives. All groups adopted a structured data science methodology. The questions formulated by the groups were categorised into seven themes, reflecting their specific areas of interest. While there was variation in the selected variables to interpret correlations, a consensus was found regarding the general results. Conclusion A significant outcome of this study is that students specialising in data science exhibited a deeper understanding of learning analytics, effectively articulating their interests through inferences drawn from their analyses. While human-led content analysis provided a general understanding of students' perspectives, the LLM offered nuanced insights.
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