How Novices Use LLM-based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment-Reference-Cited by-同舟云学术

How Novices Use LLM-based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment

Published:2023-11-13 Issue: Volume: Page:
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Container-title:Proceedings of the 23rd Koli Calling International Conference on Computing Education Research
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Author:

Kazemitabaar Majeed¹^ORCID,Hou Xinying²^ORCID,Henley Austin³^ORCID,Ericson Barbara Jane²^ORCID,Weintrop David⁴^ORCID,Grossman Tovi¹^ORCID

Affiliation:

1. Department of Computer Science, Dynamic Graphics Project (DGP), University of Toronto, Canada

2. School of Information, University of Michigan, USA

3. Microsoft Research, USA

4. College of Education, University of Maryland, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3631802.3631806

Reference55 articles.

1. Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, 2021. Program synthesis with large language models. arXiv preprint arXiv:2108.07732 (2021).

2. Detecting Student Misuse of Intelligent Tutoring Systems

3. Grounded Copilot: How Programmers Interact with Code-Generating Models

4. Programming Is Hard - Or at Least It Used to Be

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