PythonPal: Enhancing Online Programming Education Through Chatbot-Driven Personalized Feedback

IF 2.9 3区教育学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

IEEE Transactions on Learning Technologies Pub Date : 2025-02-28 DOI:10.1109/TLT.2025.3545084

Sirinda Palahan

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引用次数: 0

Abstract

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.

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PythonPal：通过聊天机器人驱动的个性化反馈增强在线编程教育

在线编程教育的兴起需要更有效的个性化交互，PythonPal旨在通过与聊天机器人集成的创新学习系统来填补这一空白。这项研究深入探讨了PythonPal在提高在线学习体验方面的潜力，特别是在学生与教师比例高的情况下，需要个性化的反馈。PythonPal的设计以对话、教程和练习模块为特色，通过学生的互动和反馈进行评估。主要发现表明，PythonPal在语法错误识别和用户查询理解方面非常熟练，其意图分类模型显示出很高的准确性。系统在误差反馈方面的性能虽然各不相同，但也显示出了优点和需要改进的地方。学生的反馈表明对查询的理解和反馈的准确性令人满意，但也指出需要更快的响应和改进的交互质量。PythonPal的部署承诺通过提供即时的个性化反馈和交互式学习体验来显著增强在线编程教育，促进学生对编程概念的更深入理解。这些好处标志着在解决远程学习挑战方面迈出了一步，使编程教育更容易获得和有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Learning Technologies COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-

CiteScore

7.50

自引率

5.40%

发文量

审稿时长

>12 weeks

期刊介绍： The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.