“It’s Weird That it Knows What I Want”: Usability and Interactions with Copilot for Novice Programmers

Author:

Prather James1ORCID,Reeves Brent N.1ORCID,Denny Paul2ORCID,Becker Brett A.3ORCID,Leinonen Juho2ORCID,Luxton-Reilly Andrew2ORCID,Powell Garrett1ORCID,Finnie-Ansley James2ORCID,Santos Eddie Antonio3ORCID

Affiliation:

1. Abilene Christian University, USA

2. The University of Auckland, New Zealand

3. University College Dublin, Ireland

Abstract

Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likely to have profound effects in the classroom, where novices learning to code can now use free tools to automatically suggest solutions to programming exercises and assignments. However, little is currently known about how novices interact with these tools in practice. We present the first study that observes students at the introductory level using one such code auto-generating tool, Github Copilot, on a typical introductory programming (CS1) assignment. Through observations and interviews we explore student perceptions of the benefits and pitfalls of this technology for learning, present new observed interaction patterns, and discuss cognitive and metacognitive difficulties faced by students. We consider design implications of these findings, specifically in terms of how tools like Copilot can better support and scaffold the novice programming experience.

Publisher

Association for Computing Machinery (ACM)

Subject

Human-Computer Interaction

Reference109 articles.

1. Trends and Trajectories for Explainable, Accountable and Intelligible Systems

2. Beyond usability

3. Plagiarism in Programming Assessments

4. Concise Graphical Representations of Student Effort on Weekly Many Small Programs

5. Desai Ankur and Deo Atul. 2022. Introducing Amazon CodeWhisperer the ML-powered Coding Companion. Retrieved from https://aws.amazon.com/blogs/machine-learning/introducing-amazon-codewhisperer-the-ml-powered-coding-companion/

Cited by 11 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Next-Step Hint Generation for Introductory Programming Using Large Language Models;Proceedings of the 26th Australasian Computing Education Conference;2024-01-29

2. Patterns of Student Help-Seeking When Using a Large Language Model-Powered Programming Assistant;Proceedings of the 26th Australasian Computing Education Conference;2024-01-29

3. The Effects of Generative AI on Computing Students’ Help-Seeking Preferences;Proceedings of the 26th Australasian Computing Education Conference;2024-01-29

4. Computing Education in the Era of Generative AI;Communications of the ACM;2024-01-25

5. Incorporating Generative AI into Software Development Education;Proceedings of the 8th Conference on Computing Education Practice;2024-01-05

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3