Online programming videos, including tutorials and streamcasts, are widely popular and contain a wealth of expert knowledge. However, effectively utilizing these resources to achieve targeted learning goals can be challenging. Unlike direct tutoring, video content lacks tailored guidance based on individual learning paces, personalized feedback, and interactive engagement necessary for support and monitoring. Our work transforms programming videos into one-on-one tutoring experiences using the cognitive apprenticeship framework. Tutorly, developed as a JupyterLab Plugin, allows learners to (1) set personalized learning goals, (2) engage in learning-by-doing through a conversational LLM-based mentor agent, (3) receive guidance and feedback based on a student model that steers the mentor moves. In a within-subject study with 16 participants learning exploratory data analysis from a streamcast, Tutorly significantly improved their performance from 61.9% to 76.6% based on a post-test questionnaire. Tutorly demonstrates the potential for enhancing programming video learning experiences with LLM and learner modeling.

翻译：在线编程视频（包括教程和直播）广受欢迎且蕴含丰富的专家知识。然而，有效利用这些资源实现针对性学习目标颇具挑战。与直接辅导不同，视频内容缺乏基于个人学习进度的定制化指导、个性化反馈以及支持与监督所需的互动参与。本研究基于认知学徒制框架，将编程视频转化为一对一辅导体验。Tutorly作为JupyterLab插件开发，支持学习者：(1)设定个性化学习目标，(2)通过基于大语言模型的对话式导师智能体进行"做中学"，(3)基于学生模型获得引导与反馈以调整导师策略。在16名参与者通过直播学习探索性数据分析的受试者内研究中，Tutorly使后测问卷成绩从61.9%显著提升至76.6%。该工作展现了利用大语言模型和学习者建模增强编程视频学习体验的潜力。