标题： 面向非正式编程学习的自我调节设计：基于叙事中心方法的见解 显示英文标题

标题： Designing for Self-Regulation in Informal Programming Learning: Insights from a Storytelling-Centric Approach

摘要： 许多人通过在线资源独立学习编程，并经常报告在实现个人学习目标时遇到困难。 学习者经常描述他们的体验是孤立和令人沮丧的，受到大量不确定性、信息过载和分心的挑战，再加上指导有限。 同时，社交媒体成为许多人进行各种自我调节实践的个人空间，包括寻求帮助、使用外部记忆辅助工具（例如自记笔记）、自我反思、情绪调节和自我激励。 例如，学习者经常通过他们的帖子标记成就并设定里程碑。 作为回应，我们开发了一个由网络平台和浏览器扩展组成的系统，以支持在线自我调节。 该设计旨在为原本无结构的体验添加学习者定义的结构，并通过将它们转化为带有AI生成反馈的学习故事，为内容整理和反思活动赋予意义。 我们将讲故事定位为一种整合性的设计方法，将资源整理、反思和意义建构实践，以及学习者在社交平台上已使用的叙述实践联系起来。 我们招募了15名非正式的编程学习者，他们都是社交媒体的常规用户，以自主节奏与系统互动；参与在提交学习故事和调查后结束。 我们使用了三个定量量表和一个定性调查来研究用户的特点以及他们对系统支持自我调节的看法。 用户反馈表明该系统作为自我调节辅助工具的可行性。 学习者特别重视现场反思、自动故事反馈和视频注释，而其他功能则收到了不同的评价。 我们强调了未来AI增强的自我调节工具的感知优势、摩擦点和设计机会。 显示英文摘要

摘要： Many people learn programming independently from online resources and often report struggles in achieving their personal learning goals. Learners frequently describe their experiences as isolating and frustrating, challenged by abundant uncertainties, information overload, and distraction, compounded by limited guidance. At the same time, social media serves as a personal space where many engage in diverse self-regulation practices, including help-seeking, using external memory aids (e.g., self-notes), self-reflection, emotion regulation, and self-motivation. For instance, learners often mark achievements and set milestones through their posts. In response, we developed a system consisting of a web platform and browser extensions to support self-regulation online. The design aims to add learner-defined structure to otherwise unstructured experiences and bring meaning to curation and reflection activities by translating them into learning stories with AI-generated feedback. We position storytelling as an integrative approach to design that connects resource curation, reflective and sensemaking practice, and narrative practices learners already use across social platforms. We recruited 15 informal programming learners who are regular social media users to engage with the system in a self-paced manner; participation concluded upon submitting a learning story and survey. We used three quantitative scales and a qualitative survey to examine users' characteristics and perceptions of the system's support for their self-regulation. User feedback suggests the system's viability as a self-regulation aid. Learners particularly valued in-situ reflection, automated story feedback, and video annotation, while other features received mixed views. We highlight perceived benefits, friction points, and design opportunities for future AI-augmented self-regulation tools.