Explaining code examples in introductory programming courses: Llm vs humans

Arun-Balajiee Lekshmi-Narayanan; Priti Oli; Jeevan Chapagain; Mohammad Hassany; Rabin Banjade; Peter Brusilovsky; Vasile Rus

Explaining code examples in introductory programming courses: Llm vs humans

Arun-Balajiee Lekshmi-Narayanan, Priti Oli, Jeevan Chapagain, Mohammad Hassany, Rabin Banjade, Peter Brusilovsky, Vasile Rus

Proceedings of the 2024 AAAI Conference on Artificial Intelligence, PMLR 257:107-117, 2024.

Abstract

Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide explanations for many examples typically used in a programming class. In this paper, we assess the feasibility of using LLMs to generate code explanations for passive and active example exploration systems. To achieve this goal, we compare the code explanations generated by chatGPT with the explanations generated by both experts and students.

Cite this Paper

BibTeX


@InProceedings{pmlr-v257-lekshmi-narayanan24a,
  title = 	 {Explaining code examples in introductory programming courses: Llm vs humans},
  author =       {Lekshmi-Narayanan, Arun-Balajiee and Oli, Priti and Chapagain, Jeevan and Hassany, Mohammad and Banjade, Rabin and Brusilovsky, Peter and Rus, Vasile},
  booktitle = 	 {Proceedings of the 2024 AAAI Conference on Artificial Intelligence},
  pages = 	 {107--117},
  year = 	 {2024},
  editor = 	 {Ananda, Muktha and Malick, Debshila Basu and Burstein, Jill and Liu, Lydia T. and Liu, Zitao and Sharpnack, James and Wang, Zichao and Wang, Serena},
  volume = 	 {257},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--27 Feb},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v257/main/assets/lekshmi-narayanan24a/lekshmi-narayanan24a.pdf},
  url = 	 {https://proceedings.mlr.press/v257/lekshmi-narayanan24a.html},
  abstract = 	 {Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide explanations for many examples typically used in a programming class. In this paper, we assess the feasibility of using LLMs to generate code explanations for passive and active example exploration systems. To achieve this goal, we compare the code explanations generated by chatGPT with the explanations generated by both experts and students.}
}

Endnote

%0 Conference Paper
%T Explaining code examples in introductory programming courses: Llm vs humans
%A Arun-Balajiee Lekshmi-Narayanan
%A Priti Oli
%A Jeevan Chapagain
%A Mohammad Hassany
%A Rabin Banjade
%A Peter Brusilovsky
%A Vasile Rus
%B Proceedings of the 2024 AAAI Conference on Artificial Intelligence
%C Proceedings of Machine Learning Research
%D 2024
%E Muktha Ananda
%E Debshila Basu Malick
%E Jill Burstein
%E Lydia T. Liu
%E Zitao Liu
%E James Sharpnack
%E Zichao Wang
%E Serena Wang	
%F pmlr-v257-lekshmi-narayanan24a
%I PMLR
%P 107--117
%U https://proceedings.mlr.press/v257/lekshmi-narayanan24a.html
%V 257
%X Worked examples, which present an explained code for solving typical programming problems are among the most popular types of learning content in programming classes. Most approaches and tools for presenting these examples to students are based on line-by-line explanations of the example code. However, instructors rarely have time to provide explanations for many examples typically used in a programming class. In this paper, we assess the feasibility of using LLMs to generate code explanations for passive and active example exploration systems. To achieve this goal, we compare the code explanations generated by chatGPT with the explanations generated by both experts and students.

APA


Lekshmi-Narayanan, A., Oli, P., Chapagain, J., Hassany, M., Banjade, R., Brusilovsky, P. & Rus, V.. (2024). Explaining code examples in introductory programming courses: Llm vs humans. Proceedings of the 2024 AAAI Conference on Artificial Intelligence, in Proceedings of Machine Learning Research 257:107-117 Available from https://proceedings.mlr.press/v257/lekshmi-narayanan24a.html.

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