  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.
