J. Prather, B. Reeves, Paul Denny, Brett A. Becker, Juho Leinonen, Andrew Luxton-Reilly, Garrett B. Powell, James Finnie-Ansley, E. Santos
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引用次数: 24
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.
期刊介绍:
This ACM Transaction seeks to be the premier archival journal in the multidisciplinary field of human-computer interaction. Since its first issue in March 1994, it has presented work of the highest scientific quality that contributes to the practice in the present and future. The primary emphasis is on results of broad application, but the journal considers original work focused on specific domains, on special requirements, on ethical issues -- the full range of design, development, and use of interactive systems.