arXiv:2507.22671cs.HCcs.AI2025-07

用故事化设计帮自学编程者记录成长,让碎片学习变有意义。

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

  • 将学习过程转化为可分享的个人故事,结合AI反馈增强反思
  • 15名用户参与测试,普遍认可即时反思与视频标注功能
  • 适合希望提升自律性、善用社交媒体的编程自学者

许多人通过在线资源自学编程,常因目标不明确、信息过载和缺乏指导而感到孤立与挫败。与此同时,社交媒体已成为人们进行自我调节的重要空间,如求助、记笔记、情绪管理、设定目标等行为普遍存在。为此,我们开发了一个包含网页平台与浏览器插件的系统,支持学习者在自主学习中建立结构化路径。系统通过将资源整理与反思活动转化为由AI生成反馈的学习故事,使日常行为获得意义。我们招募了15位经常使用社交媒体的非正式编程学习者,以自定进度方式使用该系统,最终提交学习故事与问卷。采用三项量化量表与定性调查分析用户特征及对系统的感知。结果表明,用户普遍认为系统具备可行的自我调节支持能力,尤其赞赏就地反思、自动故事反馈与视频标注功能;其他功能评价不一。研究揭示了未来人工智能增强型自我调节工具的设计机遇与改进方向。

原文摘要 · Abstract (English)

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.

自学编程自我调节故事化设计AI辅助

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