# QACP: An Annotated Question Answering Dataset for Assisting Chinese Python Programming Learners

arXiv: https://arxiv.org/abs/2402.07913 · HTML: https://mlsanity.com/paper/2402.07913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Studying the effect of AI Code Generators on Supporting Novice Learners in Introductory Programming](https://mlsanity.com/paper/2302.07427/) (2023)
- [Can ChatGPT replace StackOverflow? A Study on Robustness and Reliability of Large Language Model Code Generation](https://mlsanity.com/paper/2308.10335/) (2023)
- [Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming Courses](https://mlsanity.com/paper/2306.10073/) (2023)
- [A Comparative Study of AI-Generated (GPT-4) and Human-crafted MCQs in Programming Education](https://mlsanity.com/paper/2312.03173/) (2023)
- [Exploring the Potential of Large Language Models to Generate Formative Programming Feedback](https://mlsanity.com/paper/2309.00029/) (2023)
- [AI-TA: Towards an Intelligent Question-Answer Teaching Assistant using Open-Source LLMs](https://mlsanity.com/paper/2311.02775/) (2023)
- [Retrieval Augmented Generation and Representative Vector Summarization for large unstructured textual data in Medical Education](https://mlsanity.com/paper/2308.00479/) (2023)
- [Learning from Teaching Assistants to Program with Subgoals: Exploring the Potential for AI Teaching Assistants](https://mlsanity.com/paper/2309.10419/) (2023)
- [Can ChatGPT Play the Role of a Teaching Assistant in an Introductory Programming Course?](https://mlsanity.com/paper/2312.07343/) (2023)
- [Unmasking the giant: A comprehensive evaluation of ChatGPT's proficiency in coding algorithms and data structures](https://mlsanity.com/paper/2307.05360/) (2023)
- [External Knowledge Augmented Polyphone Disambiguation Using Large Language Model](https://mlsanity.com/paper/2312.11920/) (2023)

## Built on by (most cited first, 1 of 1)

- [Large Language Models in Computer Science Education: A Systematic Literature Review](https://mlsanity.com/paper/2410.16349/) (2024)

## Abstract (arXiv)

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.