NotePlayer：用于分析过程动态演示的交互式 Jupyter Notebook
NotePlayer: Engaging Jupyter Notebooks for Dynamic Presentation of Analytical Processes
AI总结：
本文提出 NotePlayer 工具，通过连接 notebook 单元与视频片段并集成语言模型计算引擎，简化 Jupyter 教程视频的创建与编辑，经形成性研究和用户研究验证其能有效提升分析师制作视频和沟通分析过程的效率。
摘要：
在数据分析过程中，多样化的演示格式对于有效传达代码和分析过程起着关键作用。其中一种日益流行的格式是教程视频，尤其是基于 Jupyter notebook 的教程视频，它们提供了对代码的直观解释和对分析步骤的生动说明。然而，制作此类视频需要多样化的技能组合和大量的手动工作，这对许多分析师构成了障碍。为了弥补这一差距，我们引入了一种名为 NotePlayer 的创新工具，该工具将 notebook 单元与视频片段连接起来，并结合了带有语言模型的计算引擎，以简化视频创建和编辑流程。我们的目标是使这一过程对分析师来说更易用、更高效。为了指导 NotePlayer 的设计，我们开展了一项形成性研究，并对包含 38 个 Jupyter 教程视频的语料库进行了内容分析。这帮助我们识别了现有教程视频中的关键模式和遇到的挑战，从而指导了 NotePlayer 的开发。通过结合使用场景和用户研究，我们验证了 NotePlayer 的有效性。结果表明，该工具简化了视频创建过程，并促进了数据分析师的沟通流程。
英文摘要：
Diverse presentation formats play a pivotal role in effectively conveying code and analytical processes during data analysis. One increasingly popular format is tutorial videos, particularly those based on Jupyter notebooks, which offer an intuitive interpretation of code and vivid explanations of analytical procedures. However, creating such videos requires a diverse skill set and significant manual effort, posing a barrier for many analysts. To bridge this gap, we introduce an innovative tool called NotePlayer, which connects notebook cells to video segments and incorporates a computational engine with language models to streamline video creation and editing. Our aim is to make the process more accessible and efficient for analysts. To inform the design of NotePlayer, we conducted a formative study and performed content analysis on a corpus of 38 Jupyter tutorial videos. This helped us identify key patterns and challenges encountered in existing tutorial videos, guiding the development of NotePlayer. Through a combination of a usage scenario and a user study, we validated the effectiveness of NotePlayer. The results show that the tool streamlines the video creation and facilitates the communication process for data analysts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
