Onboarding documentation is critical for attracting and retaining newcomers in open source software (OSS). However, it is often presented as dense, inconsistently structured, and fragmented presentations that are difficult to understand, which creates cognitive overload leading to frustration, errors, and abandonment. Here, we investigate how Cognitive Theory of Multimedia Learning (CTML) strategies can be used to restructure OSS documentation. We use a GenAI-based pipeline to operationalize these strategies to restructure OSS documentation through our prototype VisDoc. VisDoc segments documentation into task-based units, infers workflows, removes redundancy, and generates multimodal explanations. An expert evaluation (N=4) affirmed VisDoc's completeness, accuracy, and adoptability; A between-subjects evaluation (N=14) with newcomers found that VisDoc participants achieved higher task success, had significantly lower cognitive load, and perceived higher usability. The contributions of this work include a CTML-grounded analysis of onboarding challenges, a GenAI-based documentation restructuring pipeline, and empirical evidence that cognitively informed documentation restructuring reduces cognitive load and improves usability and task performance in OSS.

翻译：入门文档对于开源软件（OSS）吸引和留住新用户至关重要。然而，这些文档往往呈现为内容密集、结构不统一且零散的表述，难以理解，从而造成认知负荷过重，导致用户产生挫败感、出错甚至弃用。本文探究了如何运用多媒体学习认知理论（CTML）策略来重构OSS文档。我们通过原型系统VisDoc，利用基于生成式人工智能（GenAI）的流程来实现这些策略，从而重构OSS文档。VisDoc将文档分割为基于任务的单元，推断工作流程，消除冗余内容，并生成多模态解释。一项专家评估（N=4）验证了VisDoc的完整性、准确性和可采纳性；一项面向初学者的组间评估（N=14）发现，使用VisDoc的参与者在任务成功率上更高，认知负荷显著更低，且感知可用性更高。本研究的贡献包括：基于CTML理论对入门挑战的分析、基于生成式AI的文档重构流程，以及认知导向的文档重构能够减少认知负荷、提升可用性与任务绩效的实证证据。