用大模型生成学习仪表盘解释，效果优于人工和纯数据展示。
Evaluation of LLM-based Explanations for a Learning Analytics Dashboard
- 用大语言模型自动生成仪表盘数据的口语化解释。
- 教师评估显示，大模型解释更受青睐，尤其在技能状态和学习建议上。
- 适合教育科技开发者和在线课程设计者参考。
学习分析仪表盘可有效支持数字学习环境中的自我调节学习，促进元认知能力发展，如反思。然而其效果可能受数据可解释性影响。为此，我们采用大语言模型生成仪表盘数据的口头解释，并与独立仪表盘及教师提供的解释，在12名高校教育专家参与的专家研究中进行对比评估。结果表明，大语言模型生成的技能状态解释以及课程内学习推进的通用建议，显著优于其他条件。这表明利用大语言模型进行数据解释，可在保持教师认可的教学标准前提下，提升学习者的体验。
原文摘要 · Abstract (English)
Learning Analytics Dashboards can be a powerful tool to support self-regulated learning in Digital Learning Environments and promote development of meta-cognitive skills, such as reflection. However, their effectiveness can be affected by the interpretability of the data they provide. To assist in the interpretation, we employ a large language model to generate verbal explanations of the data in the dashboard and evaluate it against a standalone dashboard and explanations provided by human teachers in an expert study with university level educators (N=12). We find that the LLM-based explanations of the skill state presented in the dashboard, as well as general recommendations on how to proceed with learning within the course are significantly more favored compared to the other conditions. This indicates that using LLMs for interpretation purposes can enhance the learning experience for learners while maintaining the pedagogical standards approved by teachers.
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