PythonPal：通过聊天机器人驱动的个性化反馈强化在线编程教育
PythonPal: Enhancing Online Programming Education through Chatbot-Driven Personalized Feedback
AI总结：
本研究针对在线编程教育缺乏个性化交互的问题，开发了集成聊天机器人、含多模块的PythonPal系统，评估显示其在错误识别、查询理解等方面表现良好，可提升在线编程教育的可及性与效果。
摘要：
在线编程教育的兴起催生了对更有效、更具个性化的交互的需求，PythonPal（一款集成聊天机器人的创新学习系统）旨在填补这一缺口。本研究深入探究了PythonPal提升在线学习体验的潜力，尤其适用于师生比较高、亟需个性化反馈的场景。PythonPal的设计包含对话模块、教程模块和练习模块，研究通过学生的交互行为与反馈对其进行了评估。核心结果显示，PythonPal在语法错误识别与用户查询理解方面表现出色，其意图分类模型准确率较高；系统在错误反馈上的表现虽有差异，但既展现出优势，也存在可改进的空间。学生反馈表明，系统的查询理解能力与反馈准确率令人满意，但也指出需要提升响应速度、优化交互质量。PythonPal的落地应用有望通过提供即时个性化反馈与交互式学习体验，显著提升在线编程教育效果，帮助学生更深入地理解编程概念。这些优势为解决远程学习的挑战迈出了一步，让编程教育更易获取、更具成效。
英文摘要：
The rise of online programming education has necessitated more effective, personalized interactions, a gap that PythonPal aims to fill through its innovative learning system integrated with a chatbot. This research delves into PythonPal's potential to enhance the online learning experience, especially in contexts with high student-to-teacher ratios where there is a need for personalized feedback. PythonPal's design, featuring modules for conversation, tutorials, and exercises, was evaluated through student interactions and feedback. Key findings reveal PythonPal's proficiency in syntax error recognition and user query comprehension, with its intent classification model showing high accuracy. The system's performance in error feedback, though varied, demonstrates both strengths and areas for enhancement. Student feedback indicated satisfactory query understanding and feedback accuracy but also pointed out the need for faster responses and improved interaction quality. PythonPal's deployment promises to significantly enhance online programming education by providing immediate, personalized feedback and interactive learning experiences, fostering a deeper understanding of programming concepts among students. These benefits mark a step forward in addressing the challenges of distance learning, making programming education more accessible and effective.
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。
