| title | year | journal | abstract | layout | series | publisher | issn | id | month | tex_title | firstpage | lastpage | page | order | cycles | bibtex_author | author | date | address | container-title | volume | genre | issued | extras | |||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Explaining code examples in introductory programming courses: Llm vs humans |
2024 |
arXiv preprint arXiv:2403.05538 |
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. |
inproceedings |
Proceedings of Machine Learning Research |
PMLR |
2640-3498 |
lekshmi-narayanan24a |
0 |
Explaining code examples in introductory programming courses: Llm vs humans |
107 |
117 |
107-117 |
107 |
false |
Lekshmi-Narayanan, Arun-Balajiee and Oli, Priti and Chapagain, Jeevan and Hassany, Mohammad and Banjade, Rabin and Brusilovsky, Peter and Rus, Vasile |
|
2024-08-09 |
Proceedings of the 2024 AAAI Conference on Artificial Intelligence |
257 |
inproceedings |
|