In online learning platforms, particularly in rapidly growing computer
programming courses, addressing the thousands of students' learning queries
requires considerable human cost. The creation of intelligent assistant large
language models (LLMs) tailored for programming education necessitates distinct
data support. However, in real application scenarios, the data resources for
training such LLMs are relatively scarce. Therefore, to address the data
scarcity in intelligent educational systems for programming, this paper
proposes a new Chinese question-and-answer dataset for Python learners. To
ensure the authenticity and reliability of the sources of the questions, we
collected questions from actual student questions and categorized them
according to various dimensions such as the type of questions and the type of
learners. This annotation principle is designed to enhance the effectiveness
and quality of online programming education, providing a solid data foundation
for developing the programming teaching assists (TA). Furthermore, we conducted
comprehensive evaluations of various LLMs proficient in processing and
generating Chinese content, highlighting the potential limitations of general
LLMs as intelligent teaching assistants in computer programming courses.

为了解决编程教育中的数据稀缺问题，本文提出了一个针对 Python 学习者的新的中文问答数据集，通过收集实际学生问题并根据不同维度进行分类，以提高在线编程教育的效果和质量，并为开发编程教学助手提供扎实的数据基础。同时，通过对各种处理和生成中文内容的大型语言模型进行全面评估，凸显了通用大型语言模型在计算机编程课程中作为智能教学助手的潜在局限性。