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.

本研究解决了当前知识追踪方法过于依赖统计数据和人工定义知识组件的问题。通过使用经过指令调优的大型多模态模型，我们提出了一种自动提取教育内容中知识组件的新方法。研究结果表明，自动提取的知识组件可以有效替代人工标记的标签，为智能辅导系统的改进提供了可行路径。

利用大型多模态模型从多媒体问题信息中提取知识组件用于知识追踪