[论文解读] Smart tutor to provide feedback in programming courses

David Roldán‐Álvarez|arXiv (Cornell University)|Jan 24, 2023

E-Learning and Knowledge ManagementComputer Science被引用 3

一句话总结

本文提出了一种基于人工智能的智能导师系统，旨在为学习编程的大学生提供实时、个性化的反馈，利用自然语言处理技术理解并回应学生的编程问题。该系统在URJC大学的一门完整课程中进行了测试，结果表明其在提供准确答案和示例方面表现出色，学生认为该系统易于使用，且可能对其他课程具有潜在价值。

ABSTRACT

Artificial Intelligence (AI) is becoming more and more popular as time passes, allowing to perform tasks that were difficult to do in the past. From predictions to customization, AI is being used in many areas, not being educational environments outside this situation. AI is being used in educational settings to customize contents or to provide personalized feedback to the students, among others. In this scenario, AI in programming teaching is something that still has to be explored, since in this area we usually find assessment tools that allow grading the students work, but we can not find many tools aimed towards providing feedback to the students in the process of creating their program. In this work we present an AI based intelligent tutor that answers students programming questions. The tool has been tested by university students at the URJC along a whole course. Even if the tool is still in its preliminary phase, it helped the students with their questions, providing accurate answers and examples. The students were able to use the intelligent tutor easily and they thought that it could be a useful tool to use in other courses.

研究动机与目标

• 为解决在编程开发过程中缺乏提供形成性反馈的AI工具的问题，超越自动评分功能。
• 设计一种能够理解学生编程问题并提供准确、上下文感知响应的智能导师。
• 在真实的大学编程课程环境中，评估该导师的可用性和感知有用性。
• 探索人工智能在通过即时、基于示例的反馈提升学生学习效果方面的潜力。

提出的方法

• 该系统采用自然语言处理（NLP）技术，解析学生关于其代码的文本问题。
• 从针对常见编程错误和概念定制的知识库中检索相关反馈和代码示例。
• 通过将学生查询与知识库中的预定义模式和解决方案匹配，生成响应。
• 该工具被集成到URJC大学的一门编程课程中，实现整个学期的部署与评估。
• 记录学生互动数据，以评估响应准确性和用户体验。
• 基于学生感知和系统在回答问题方面的性能，对反馈进行评估。

实验结果

研究问题

• RQ1 基于人工智能的导师能否有效理解并回应学生关于其编程代码的自然语言问题？
• RQ2 在真实的课程环境中，学生如何评价该智能导师的可用性和帮助性？
• RQ3 该导师在通过及时、准确的反馈改善学生学习方面，其影响程度如何？
• RQ4 此类系统是否可在大学级别的编程教育中实现实际部署与采用？

主要发现

• 该智能导师在完整学期内成功为学生的编程问题提供了准确且相关性强的反馈。
• 学生表示该工具易于使用，并认为其可能对今后的课程具有潜在帮助价值。
• 该系统在提供基于示例的响应方面表现出可行性，有助于学生理解。
• 尽管仍处于初步阶段，该工具在提升编程教育中的形成性反馈方面展现出良好前景。