@inproceedings{fan-etal-2023-exploring,
title = "Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses",
author = "Fan, Aysa and
Zhang, Haoran and
Paquette, Luc and
Zhang, Rui",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.496",
doi = "10.18653/v1/2023.findings-emnlp.496",
pages = "7406--7421",
abstract = "In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts for GPT4, guiding it to generate code-tracing questions based on code snippets and descriptions. We established a set of human evaluation metrics to assess the quality of questions produced by the model compared to those created by human experts. Our analysis provides insights into the capabilities and potential of LLMs in generating diverse code-tracing questions. Additionally, we present a unique dataset of human and LLM-generated tracing questions, serving as a valuable resource for both the education and NLP research communities. This work contributes to the ongoing dialogue on the potential uses of LLMs in educational settings.",
}
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<abstract>In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts for GPT4, guiding it to generate code-tracing questions based on code snippets and descriptions. We established a set of human evaluation metrics to assess the quality of questions produced by the model compared to those created by human experts. Our analysis provides insights into the capabilities and potential of LLMs in generating diverse code-tracing questions. Additionally, we present a unique dataset of human and LLM-generated tracing questions, serving as a valuable resource for both the education and NLP research communities. This work contributes to the ongoing dialogue on the potential uses of LLMs in educational settings.</abstract>
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%0 Conference Proceedings %T Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses %A Fan, Aysa %A Zhang, Haoran %A Paquette, Luc %A Zhang, Rui %Y Bouamor, Houda %Y Pino, Juan %Y Bali, Kalika %S Findings of the Association for Computational Linguistics: EMNLP 2023 %D 2023 %8 December %I Association for Computational Linguistics %C Singapore %F fan-etal-2023-exploring %X In this paper, we explore the application of large language models (LLMs) for generating code-tracing questions in introductory programming courses. We designed targeted prompts for GPT4, guiding it to generate code-tracing questions based on code snippets and descriptions. We established a set of human evaluation metrics to assess the quality of questions produced by the model compared to those created by human experts. Our analysis provides insights into the capabilities and potential of LLMs in generating diverse code-tracing questions. Additionally, we present a unique dataset of human and LLM-generated tracing questions, serving as a valuable resource for both the education and NLP research communities. This work contributes to the ongoing dialogue on the potential uses of LLMs in educational settings. %R 10.18653/v1/2023.findings-emnlp.496 %U https://aclanthology.org/2023.findings-emnlp.496 %U https://doi.org/10.18653/v1/2023.findings-emnlp.496 %P 7406-7421
Markdown (Informal)
[Exploring the Potential of Large Language Models in Generating Code-Tracing Questions for Introductory Programming Courses](https://aclanthology.org/2023.findings-emnlp.496) (Fan et al., Findings 2023)
