用大模型解析学生点击流，揭示学习策略
ClickSight: Interpreting Student Clickstreams to Reveal Insights on Learning Strategies via LLMs
- 基于大模型的提示工程解析原始点击数据
- 不同提示策略影响解读质量，自修正提升有限
- 适合教育数据挖掘与教学分析研究者使用
数字学习环境中的点击流数据能反映学生的学习行为，但因维度高、粒度细，难以解释。以往方法多依赖人工特征、专家标注、聚类或有监督模型，普遍存在泛化性和可扩展性不足的问题。本文提出 ClickSight，一种基于上下文大语言模型（LLM）的点击流解析框架，输入原始点击流和学习策略列表，输出对学生交互行为的文本解读。我们评估了四种提示策略，并研究了自修正对解读质量的影响。在两个开放学习环境上进行评估，采用专家评分体系。结果表明，大模型可合理解读学习策略，但解读质量受提示策略影响显著，自修正改进有限。该方法展示了大模型从教育交互数据中生成理论驱动洞察的潜力。
原文摘要 · Abstract (English)
Clickstream data from digital learning environments offer valuable insights into students' learning behaviors, but are challenging to interpret due to their high dimensionality and granularity. Prior approaches have relied mainly on handcrafted features, expert labeling, clustering, or supervised models, therefore often lacking generalizability and scalability. In this work, we introduce ClickSight, an in-context Large Language Model (LLM)-based pipeline that interprets student clickstreams to reveal their learning strategies. ClickSight takes raw clickstreams and a list of learning strategies as input and generates textual interpretations of students' behaviors during interaction. We evaluate four different prompting strategies and investigate the impact of self-refinement on interpretation quality. Our evaluation spans two open-ended learning environments and uses a rubric-based domain-expert evaluation. Results show that while LLMs can reasonably interpret learning strategies from clickstreams, interpretation quality varies by prompting strategy, and self-refinement offers limited improvement. ClickSight demonstrates the potential of LLMs to generate theory-driven insights from educational interaction data.
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