Evaluating the Quality of LLM-Generated Explanations for Logical Errors in CS1 Student Programs

Author:

Balse Rishabh1ORCID,Kumar Viraj2ORCID,Prasad Prajish3ORCID,Warriem Jayakrishnan Madathil4ORCID

Affiliation:

1. BS Programme in Data Science and Applications, Indian Institute of Technology Madras, India

2. Indian Institute of Science, India

3. School of Computing and Data Sciences, FLAME University, India

4. National Programme on Technology Enhanced Learning (NPTEL), Indian Institute of Technology Madras, India and BS Programme in Data Science and Applications, Indian Institute of Technology Madras, India

Publisher

ACM

Reference26 articles.

1. Investigating the Potential of GPT-3 in Providing Feedback for Programming Assessments

2. Compiler Error Messages Considered Unhelpful

3. D. Boud and E. Molloy. 2013. Feedback in Higher and Professional Education: Understanding it and Doing it Well. Routledge. https://books.google.co.in/books?id=0N8R7DRwKf0C D. Boud and E. Molloy. 2013. Feedback in Higher and Professional Education: Understanding it and Doing it Well. Routledge. https://books.google.co.in/books?id=0N8R7DRwKf0C

4. Julio  C Caiza and Jose  M Del Alamo . 2013 . Programming assignments automatic grading: review of tools and implementations . INTED2013 Proceedings (2013), 5691–5700. Julio C Caiza and Jose M Del Alamo. 2013. Programming assignments automatic grading: review of tools and implementations. INTED2013 Proceedings (2013), 5691–5700.

5. Mark Chen , Jerry Tworek , Heewoo Jun , Qiming Yuan , Henrique Ponde de Oliveira Pinto , Jared Kaplan, Harri Edwards, Yuri Burda , Nicholas Joseph, Greg Brockman , 2021 . Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021). Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, 2021. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021).

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