用大模型自动提取知识点，让智能辅导更高效。
Using Large Multimodal Models to Extract Knowledge Components for Knowledge Tracing from Multimedia Question Information
- 用指令微调的多模态大模型自动解析多媒体题目的知识点
- 在五个领域基准测试中表现接近人工标注标签
- 适合想低成本构建智能辅导系统的研究者和教育科技开发者
知识追踪模型已广泛应用于智能辅导系统以提供学生反馈。然而，现有学习科学中的知识追踪方法主要依赖统计数据和教师定义的知识点，难以与AI生成的教育内容融合。本文提出一种基于指令微调的大规模多模态模型，自动从教育内容中提取知识点。我们在五个领域上全面评估该方法，结果表明，自动生成的知识点可有效替代人工标注标签，为有限数据场景下的智能辅导系统提供可解释性评估，并奠定自动化评估的基础。
原文摘要 · Abstract (English)
Knowledge tracing models have enabled a range of intelligent tutoring systems to provide feedback to students. However, existing methods for knowledge tracing in learning sciences are predominantly reliant on statistical data and instructor-defined knowledge components, making it challenging to integrate AI-generated educational content with traditional established methods. We propose a method for automatically extracting knowledge components from educational content using instruction-tuned large multimodal models. We validate this approach by comprehensively evaluating it against knowledge tracing benchmarks in five domains. Our results indicate that the automatically extracted knowledge components can effectively replace human-tagged labels, offering a promising direction for enhancing intelligent tutoring systems in limited-data scenarios, achieving more explainable assessments in educational settings, and laying the groundwork for automated assessment.
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