TeachUp：借助反思性支持帮助新手教师从课堂视频中学习教学策略
TeachUp: Facilitating Early-Stage Teachers to Learn Instructional Strategies from Classroom Videos with Reflective Support
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中文总结 AI 辅助
TeachUp借助LLM驱动的流程检测课堂视频中的9种教学策略，为新手教师提供反思支持，经实验验证可提升其学习参与度与策略应用表现。
中文摘要 AI 辅助
线下公开课的录制视频为新手教师学习教学策略提供了良好范例，例如如何组织合作学习。然而，观看这些视频学习颇具挑战，因为这些策略是隐性呈现的，且缺乏原位反思支持。本文通过一项包含9名参与者的形成性研究，设计了TeachUp以支持从课堂教学视频中学习教学策略。TeachUp采用大语言模型（LLM）驱动的流程，检测视频中的9种教学策略（精确率为63.4%），在观看时提供反思问题和提示，并生成带有反思反馈的定制化练习。一项包含16名参与者的被试内研究显示，与传统的视频播放和自主练习基线相比，使用TeachUp的新手教师学习参与度更高，且在将所学策略应用于新任务时表现更优。对4名在职教师的访谈进一步推广了我们的发现及TeachUp的应用场景，我们探讨了促进基于视频的教学策略学习的实践意义。
英文摘要
Recorded videos of offline open classes provide good examples for early-stage teachers to learn instructional strategies, e.g., how to organize cooperative learning. However, learning by watching these videos is challenging, as these strategies are implicitly performed, and it lacks in-situ reflective support. In this paper, via a formative study (N=9), we design TeachUp to support the learning of instructional strategies from classroom teaching videos. TeachUp adopts an LLM-powered pipeline to detect nine instructional strategies in videos (precision = 63.4%), provides reflective questions and hints while watching, and generates customized practices with reflective feedback. A within-subjects study (N=16) shows that compared to a traditional video-playing and self-practicing baseline, early-stage teachers with TeachUp are more engaged in learning and perform better in applying learned strategies to new tasks. Interviews with four in-service teachers further generalize our findings and TeachUp's use cases. We discuss practical implications for fostering video-based learning of instructional strategies.
