发表机构
Carnegie Mellon University; Renaissance Philanthropy; Vanderbilt University（卡内基梅隆大学; 文艺复兴慈善机构; 范德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出AI驱动的多模态转录系统，将屏幕录制视频转为剧本式转录，并计划与MATHia日志对齐，以突破学习系统壁垒，实现跨平台学习建模。
AI 中文摘要
过去使用日志数据的研究面临“学习系统壁垒”问题，即缺乏跨平台泛化学生学学习模型的方法。如今，在线学习越来越多地通过更丰富的数据形式（包括对话和视频）被捕获，并带来新的功能。一个例子是远程辅导项目，其中人类导师在视频会议中支持使用学习系统的学生。为了更好地对学习进行平台通用建模，我们引入了一个AI驱动的多模态转录系统，该系统将屏幕录制视频处理为统一的剧本式转录，包含音频对话和标注的学习日志操作。我们描述了一种计划中的方法，用于将AI生成的多模态转录与MATHia学习日志进行时间对齐，并识别和分类学生学习过程以与MATHia日志对齐。最后，我们强调了在一个系统中捕获学习过程的挑战和潜在解决方案，为跨不同系统泛化日志数据提供了初步步骤。
英文摘要
Past research using log data has faced the "learning system wall," whereby few methods exist for generalizing models of student learning across platforms. Increasingly, online learning is captured by richer forms of data, including dialog and video, with new affordances. An example of this is remote tutoring programs, where human tutors support students who use learning systems while video conferencing. Toward better platform-general modeling of learning, we introduce an AI-driven multimodal transcription system that processes screen-recording videos into unified screenplay-style transcripts containing audio dialogue and annotated learning log actions. We describe a planned method for temporally aligning AI-generated multimodal transcripts with MATHia learning logs and for identifying and classifying student learning processes to align with MATHia logs. Lastly, we highlight challenges and potential solutions in capturing learning processes in one system, offering initial steps towards generalizing log data across diverse systems.
CommentsFull paper accepted to the AIME Conference 2026
