Summary of Qacp: An Annotated Question Answering Dataset For Assisting Chinese Python Programming Learners, by Rui Xiao et al.
QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners
by Rui Xiao, Lu Han, Xiaoying Zhou, Jiong Wang, Na Zong, Pengyu Zhang
First submitted to arxiv on: 30 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research aims to address the issue of data scarcity in intelligent educational systems for programming. To achieve this, a new dataset is created specifically designed for Python learners. The dataset consists of Chinese question-and-answer pairs collected from actual student questions and categorized according to various dimensions. This novel approach enhances the effectiveness and quality of online programming education, providing a solid foundation for developing teaching assistants (TAs). Furthermore, the study evaluates various large language models proficient in processing and generating Chinese content, highlighting their potential limitations as intelligent TAs in computer programming courses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new dataset to help teach programming. It collects questions from real students and organizes them in a special way. This helps make online programming education better and more effective. The study also looks at how well language models can be used to assist teaching, and finds that they have some limitations. |
